{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R语言PCA分析教程 Principal Component Methods in R  \n",
    "主成分分析Principal Component Methods（PCA）允许我们总结和可视化包含由多个相互关联的定量变量描述的个体/观察的数据集中的信息。每个变量都可以视为不同的维度。如果数据集中包含3个以上的变量，那么可视化多维超空间可能非常困难。  \n",
    "主成分分析用于从多变量数据表中提取重要信息，并将此信息表示为一组称为主成分的新变量。这些新变量对应于原件的线性组合。主成分的数量小于或等于原始变量的数量。PCA的目标是识别数据变化最大的方向（或主成分）。换句话说，PCA将多变量数据的维度降低到两个或三个主要成分，这些成分可以图形化可视化，同时信息损失最小。PCA属于机器学习降维方法质之一，但是仅仅对线性数据有用，非线性数据建议使用TSNE。  \n",
    "本文描述了PCA的基本概念，并演示了如何使用R软件计算和可视化PCA。此外，我们将展示如何揭示解释数据集变化的最重要变量。主要内容如下：\n",
    "1. 基础\n",
    "2. 计算\n",
    "3. 实例\n",
    "4. 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 基础  \n",
    "+ 基础概念  \n",
    "+ 计算过程  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 基础概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "了解PCA的细节需要线性代数的知识。在这里，我们将仅通过简单的数据图形表示来解释基础知识。\n",
    "在下图1A中，数据在XY坐标系中表示。通过识别数据变化的主要方向（称为主成分）来实现降维。PCA假设方差最大的方向是最“重要的”（即最主要的方向）。\n",
    "在下图1A中，PC1轴是样本显示最大变化的第一个主方向。PC2轴是第二个最重要的方向，它与PC1轴正交。通过将每个样本投影到第一个主成分上，我们的二维数据的维数可以减少到一个维度（图1B）。  \n",
    "![1A](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/1A.png)\n",
    "![1B](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/1B.png)\n",
    "从技术上讲，每个主成分保留的方差量是通过所谓的特征值来测量的。请注意，当数据集中的变量高度相关时，PCA方法特别有用。相关性表明数据存在冗余。由于这种冗余，PCA可用于将原始变量减少为较少数量的新变量（= 主成分），解释原始变量的大部分方差。\n",
    "![2A](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/2A.png)\n",
    "![2B](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/2B.png)\n",
    "总的来说，主成分分析的主要目的是：\n",
    "\n",
    "+ 识别数据集中的隐藏模式；\n",
    "+ 通过消除数据中的噪声和冗余来降低数据的维度；\n",
    "+ 识别相关变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 计算过程  \n",
    "目前很多书籍讲PCA并不那么通俗易懂。PCA是用来降低数据维度的，维度降低后的主成分和原来的变量不是一个东西。如果想获得原来数据中重要的变量，删除无关变量，请参考特征工程。PCA计算过程很简单，具体如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）数据处理**  \n",
    "假设我们有这样的2维数据：\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.1.png)  \n",
    "其中行代表了样例，列代表特征，这里有10个样例可以认为有10辆汽车，x是千米/小时的速度，y是英里/小时的速度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常x,y都是不同的变量，如果要放到一起来比较，一般都要进行数据标准化使得各个变量数据能够放到一块比较。这里简单一点，只减去平均值。分别求x和y的平均值，然后对于所有的样例，都减去对应的均值。这里x的均值是1.81，y的均值是1.91，那么一个样例减去均值后即为（0.69,0.49），得到\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.2.png) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(2) 计算特征值**  \n",
    "在现实情况下，我们需要通过计算数值矩阵的相关系数矩阵或者协方差矩阵来求得特征值和特征向量，进而获得主要成分。相关系数矩阵和协方差矩阵能够变量间相关性，主成分分析会删除和其他变量相关性强的变量，留下更具有代表性的变量。相关系数矩阵相当于消除量纲的表示变量间相关性的一个矩阵，协方差系数矩阵是没有消除量纲的表示变量间相关性的矩阵。相关系数矩阵是协方差系数矩阵的特例，通常在PCA中，如果数据量很少用相关系数矩阵，很多用协方差系数矩阵。  \n",
    "这里主要计算协方差系数矩阵，因为协方差系数更具有实际代表意义。主要计算过程网上都有。那么我们能够获得矩阵的特征值和特征向量（这部分是线性代数的内容)。如下所示：\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.3.png)\n",
    "这里特征值0.049对应特征向量为$（-0.735,0.677)^T$;特征值1.284对应特征向量为$（-0.677,-0.735)^T$。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主成分分析降维的意思就是根据特征值的大小挑选主成分变量，比如这里我们要把二维数据降为一维，就选取最大特征值1.284对应的特征向量计算主成分得分。计算公式如下：   \n",
    "$PC1= DataAdjust×特征向量(-0.677, -0.735)^T$   \n",
    "PC1就是我们说的主成分得分，特征向量(-0.677, -0.735)就是我们说得主成分系数。我们所获得降维后的一维变量就是通过这个公式对每行数据通过上面公式获得的。结果如下：\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(3) 可视化描述**  \n",
    "上述过程可以简单用图来描述，我们有一个经过归一化的数据，这个数据各个样本点都是分散的，无规律的。\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.5.png)\n",
    "如果我们将原数据降为二维（这里实际维度没有变化，一维不好表示）。那么结果如下：\n",
    "![png](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/3.6.png)\n",
    "可以看到现在各个样本点分布像一条直线，与x轴平行。样本点的x坐标就是第一主成分，第二主成分就是y坐标。\n",
    "其中贡献率是表示投影后信息的保留程度的变量，计算公式就是前K个特征值除以总的特征值之和。计算公公式如下：\n",
    "$$\n",
    "\\frac{\\sum_{i=0}^{K} \\lambda_{i}}{\\sum_{i=0}^{m} \\lambda_{i}}\n",
    "$$\n",
    "比如本文例子有两个特征值1.284和0.049，如果我们降为一维主成分，那么第一主成分贡献率为$1.284/(1.284+0.049)=0.963$。\n",
    "\n",
    "PCA详细原理说明见：https://blog.csdn.net/LuohenYJ/article/details/78355954"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 计算\n",
    "+ R包\n",
    "+ R代码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 R包\n",
    "用于计算PCA 的R软件中提供了来自不同软件包的多个函数：\n",
    "+ prcomp()和princomp() [内置]，\n",
    "+ PCA() [ FactoMineR包]，\n",
    "+ dudi.pca() [ ade4包]，\n",
    "+ epPCA() [ ExPosition包]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无论您决定使用什么功能，您都可以使用factoextraR包中提供的R功能轻松提取和可视化PCA的结果。\n",
    "通过install.packages(\"FactoMineR\", \"factoextra\")安装所使用的包。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'FactoMineR' was built under R version 3.6.1\"Loading required package: ggplot2\n",
      "Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ\n"
     ]
    }
   ],
   "source": [
    "# 调用R包\n",
    "library(\"FactoMineR\");\n",
    "library(\"factoextra\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将使用演示数据集decathlon2从factoextra包，数据集如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X100m</th><th scope=col>Long.jump</th><th scope=col>Shot.put</th><th scope=col>High.jump</th><th scope=col>X400m</th><th scope=col>X110m.hurdle</th><th scope=col>Discus</th><th scope=col>Pole.vault</th><th scope=col>Javeline</th><th scope=col>X1500m</th><th scope=col>Rank</th><th scope=col>Points</th><th scope=col>Competition</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SEBRLE</th><td>11.04</td><td>7.58</td><td>14.83</td><td>2.07</td><td>49.81</td><td>14.69</td><td>43.75</td><td>5.02</td><td>63.19</td><td>291.7</td><td>1</td><td>8217</td><td>Decastar</td></tr>\n",
       "\t<tr><th scope=row>CLAY</th><td>10.76</td><td>7.40</td><td>14.26</td><td>1.86</td><td>49.37</td><td>14.05</td><td>50.72</td><td>4.92</td><td>60.15</td><td>301.5</td><td>2</td><td>8122</td><td>Decastar</td></tr>\n",
       "\t<tr><th scope=row>BERNARD</th><td>11.02</td><td>7.23</td><td>14.25</td><td>1.92</td><td>48.93</td><td>14.99</td><td>40.87</td><td>5.32</td><td>62.77</td><td>280.1</td><td>4</td><td>8067</td><td>Decastar</td></tr>\n",
       "\t<tr><th scope=row>YURKOV</th><td>11.34</td><td>7.09</td><td>15.19</td><td>2.10</td><td>50.42</td><td>15.31</td><td>46.26</td><td>4.72</td><td>63.44</td><td>276.4</td><td>5</td><td>8036</td><td>Decastar</td></tr>\n",
       "\t<tr><th scope=row>ZSIVOCZKY</th><td>11.13</td><td>7.30</td><td>13.48</td><td>2.01</td><td>48.62</td><td>14.17</td><td>45.67</td><td>4.42</td><td>55.37</td><td>268.0</td><td>7</td><td>8004</td><td>Decastar</td></tr>\n",
       "\t<tr><th scope=row>McMULLEN</th><td>10.83</td><td>7.31</td><td>13.76</td><td>2.13</td><td>49.91</td><td>14.38</td><td>44.41</td><td>4.42</td><td>56.37</td><td>285.1</td><td>8</td><td>7995</td><td>Decastar</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & X100m & Long.jump & Shot.put & High.jump & X400m & X110m.hurdle & Discus & Pole.vault & Javeline & X1500m & Rank & Points & Competition\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <int> & <fct>\\\\\n",
       "\\hline\n",
       "\tSEBRLE & 11.04 & 7.58 & 14.83 & 2.07 & 49.81 & 14.69 & 43.75 & 5.02 & 63.19 & 291.7 & 1 & 8217 & Decastar\\\\\n",
       "\tCLAY & 10.76 & 7.40 & 14.26 & 1.86 & 49.37 & 14.05 & 50.72 & 4.92 & 60.15 & 301.5 & 2 & 8122 & Decastar\\\\\n",
       "\tBERNARD & 11.02 & 7.23 & 14.25 & 1.92 & 48.93 & 14.99 & 40.87 & 5.32 & 62.77 & 280.1 & 4 & 8067 & Decastar\\\\\n",
       "\tYURKOV & 11.34 & 7.09 & 15.19 & 2.10 & 50.42 & 15.31 & 46.26 & 4.72 & 63.44 & 276.4 & 5 & 8036 & Decastar\\\\\n",
       "\tZSIVOCZKY & 11.13 & 7.30 & 13.48 & 2.01 & 48.62 & 14.17 & 45.67 & 4.42 & 55.37 & 268.0 & 7 & 8004 & Decastar\\\\\n",
       "\tMcMULLEN & 10.83 & 7.31 & 13.76 & 2.13 & 49.91 & 14.38 & 44.41 & 4.42 & 56.37 & 285.1 & 8 & 7995 & Decastar\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | X100m &lt;dbl&gt; | Long.jump &lt;dbl&gt; | Shot.put &lt;dbl&gt; | High.jump &lt;dbl&gt; | X400m &lt;dbl&gt; | X110m.hurdle &lt;dbl&gt; | Discus &lt;dbl&gt; | Pole.vault &lt;dbl&gt; | Javeline &lt;dbl&gt; | X1500m &lt;dbl&gt; | Rank &lt;int&gt; | Points &lt;int&gt; | Competition &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| SEBRLE | 11.04 | 7.58 | 14.83 | 2.07 | 49.81 | 14.69 | 43.75 | 5.02 | 63.19 | 291.7 | 1 | 8217 | Decastar |\n",
       "| CLAY | 10.76 | 7.40 | 14.26 | 1.86 | 49.37 | 14.05 | 50.72 | 4.92 | 60.15 | 301.5 | 2 | 8122 | Decastar |\n",
       "| BERNARD | 11.02 | 7.23 | 14.25 | 1.92 | 48.93 | 14.99 | 40.87 | 5.32 | 62.77 | 280.1 | 4 | 8067 | Decastar |\n",
       "| YURKOV | 11.34 | 7.09 | 15.19 | 2.10 | 50.42 | 15.31 | 46.26 | 4.72 | 63.44 | 276.4 | 5 | 8036 | Decastar |\n",
       "| ZSIVOCZKY | 11.13 | 7.30 | 13.48 | 2.01 | 48.62 | 14.17 | 45.67 | 4.42 | 55.37 | 268.0 | 7 | 8004 | Decastar |\n",
       "| McMULLEN | 10.83 | 7.31 | 13.76 | 2.13 | 49.91 | 14.38 | 44.41 | 4.42 | 56.37 | 285.1 | 8 | 7995 | Decastar |\n",
       "\n"
      ],
      "text/plain": [
       "          X100m Long.jump Shot.put High.jump X400m X110m.hurdle Discus\n",
       "SEBRLE    11.04 7.58      14.83    2.07      49.81 14.69        43.75 \n",
       "CLAY      10.76 7.40      14.26    1.86      49.37 14.05        50.72 \n",
       "BERNARD   11.02 7.23      14.25    1.92      48.93 14.99        40.87 \n",
       "YURKOV    11.34 7.09      15.19    2.10      50.42 15.31        46.26 \n",
       "ZSIVOCZKY 11.13 7.30      13.48    2.01      48.62 14.17        45.67 \n",
       "McMULLEN  10.83 7.31      13.76    2.13      49.91 14.38        44.41 \n",
       "          Pole.vault Javeline X1500m Rank Points Competition\n",
       "SEBRLE    5.02       63.19    291.7  1    8217   Decastar   \n",
       "CLAY      4.92       60.15    301.5  2    8122   Decastar   \n",
       "BERNARD   5.32       62.77    280.1  4    8067   Decastar   \n",
       "YURKOV    4.72       63.44    276.4  5    8036   Decastar   \n",
       "ZSIVOCZKY 4.42       55.37    268.0  7    8004   Decastar   \n",
       "McMULLEN  4.42       56.37    285.1  8    7995   Decastar   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(decathlon2)\n",
    "head(decathlon2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但我们只选择部分数据进行计算，处理如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X100m</th><th scope=col>Long.jump</th><th scope=col>Shot.put</th><th scope=col>High.jump</th><th scope=col>X400m</th><th scope=col>X110m.hurdle</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SEBRLE</th><td>11.04</td><td>7.58</td><td>14.83</td><td>2.07</td><td>49.81</td><td>14.69</td></tr>\n",
       "\t<tr><th scope=row>CLAY</th><td>10.76</td><td>7.40</td><td>14.26</td><td>1.86</td><td>49.37</td><td>14.05</td></tr>\n",
       "\t<tr><th scope=row>BERNARD</th><td>11.02</td><td>7.23</td><td>14.25</td><td>1.92</td><td>48.93</td><td>14.99</td></tr>\n",
       "\t<tr><th scope=row>YURKOV</th><td>11.34</td><td>7.09</td><td>15.19</td><td>2.10</td><td>50.42</td><td>15.31</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & X100m & Long.jump & Shot.put & High.jump & X400m & X110m.hurdle\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tSEBRLE & 11.04 & 7.58 & 14.83 & 2.07 & 49.81 & 14.69\\\\\n",
       "\tCLAY & 10.76 & 7.40 & 14.26 & 1.86 & 49.37 & 14.05\\\\\n",
       "\tBERNARD & 11.02 & 7.23 & 14.25 & 1.92 & 48.93 & 14.99\\\\\n",
       "\tYURKOV & 11.34 & 7.09 & 15.19 & 2.10 & 50.42 & 15.31\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 6\n",
       "\n",
       "| <!--/--> | X100m &lt;dbl&gt; | Long.jump &lt;dbl&gt; | Shot.put &lt;dbl&gt; | High.jump &lt;dbl&gt; | X400m &lt;dbl&gt; | X110m.hurdle &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| SEBRLE | 11.04 | 7.58 | 14.83 | 2.07 | 49.81 | 14.69 |\n",
       "| CLAY | 10.76 | 7.40 | 14.26 | 1.86 | 49.37 | 14.05 |\n",
       "| BERNARD | 11.02 | 7.23 | 14.25 | 1.92 | 48.93 | 14.99 |\n",
       "| YURKOV | 11.34 | 7.09 | 15.19 | 2.10 | 50.42 | 15.31 |\n",
       "\n"
      ],
      "text/plain": [
       "        X100m Long.jump Shot.put High.jump X400m X110m.hurdle\n",
       "SEBRLE  11.04 7.58      14.83    2.07      49.81 14.69       \n",
       "CLAY    10.76 7.40      14.26    1.86      49.37 14.05       \n",
       "BERNARD 11.02 7.23      14.25    1.92      48.93 14.99       \n",
       "YURKOV  11.34 7.09      15.19    2.10      50.42 15.31       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decathlon2.active <- decathlon2[1:23, 1:10]\n",
    "head(decathlon2.active[, 1:6], 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在主成分分析中，变量通常被缩放（即标准化）。当变量以不同的尺度（例如：千克，千米，厘米......）测量时，尤其建议这样做; 否则，获得的PCA输出将受到严重影响。目标是使变量具有可比性。通常，变量被缩放为具有标准偏差1和平均值为零。数据标准化是在PCA和聚类分析之前广泛用于基因表达数据分析的方法。当变量的平均值和/或标准偏差大不相同时，我们可能还希望缩放数据。缩放变量时，数据转换公式如下：\n",
    "$$\n",
    "\\frac{x_{i}-\\operatorname{mean}(x)}{s d(x)}\n",
    "$$\n",
    "这种方法是计算相关系数矩阵，默认是计算相关系数矩阵而不是协方差系数矩阵。请注意，默认情况下在FactoMineR 中，PCA之前会自动标准化数据; 所以你不需要在PCA之前进行这种转换。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 R代码  \n",
    "本部分主要介绍R语言FactoMineR进行PCA的常用代码，具体实例见下一章。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PCA(X, scale.unit = TRUE, ncp = 5, graph = TRUE)**\n",
    "+ X：数据框。行是个体，列是数字变量\n",
    "+ scale.unit：一个逻辑值。如果为TRUE，则在分析之前将数据缩放为单位方差。这种相同规模的标准化避免了一些变量因其较大的测量单位而成为主导。它使变量具有可比性。\n",
    "+ ncp：最终结果中保留的维数。\n",
    "+ graph：一个逻辑值。如果为TRUE，则显示图表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将使用factoextra R包来帮助解释PCA。无论你决定使用什么函数，你都可以轻松地提取和可视化PCA的结果使用factoextra R包中提供的R函数。这些功能包括：\n",
    "+ **get_eigenvalue(res.pca)**：提取主成分的特征值/方差\n",
    "+ **fviz_eig(res.pca)**：可视化特征值\n",
    "+ **get_pca_ind(res.pca)**，get_pca_var(res.pca)：分别提取个体和变量的结果。\n",
    "+ **fviz_pca_ind(res.pca)**，fviz_pca_var(res.pca)：分别可视化结果个体和变量。\n",
    "+ **fviz_pca_biplot(res.pca)**：制作主成分分析散点图biplot图。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们检查特征值以确定考虑的主成分的数量。可以使用函数get_eigenvalue提取主成分保留的特征值和方差（即信息）的比例。  \n",
    "**eig.val <- get_eigenvalue(res.pca)**   \n",
    "**eig.val**    \n",
    "一般打印结果如下：\n",
    "```\n",
    "##        eigenvalue variance.percent cumulative.variance.percent\n",
    "## Dim.1       4.124            41.24                        41.2\n",
    "## Dim.2       1.839            18.39                        59.6\n",
    "## Dim.3       1.239            12.39                        72.0\n",
    "## Dim.4       0.819             8.19                        80.2\n",
    "## Dim.5       0.702             7.02                        87.2\n",
    "## Dim.6       0.423             4.23                        91.5\n",
    "## Dim.7       0.303             3.03                        94.5\n",
    "## Dim.8       0.274             2.74                        97.2\n",
    "## Dim.9       0.155             1.55                        98.8\n",
    "## Dim.10      0.122             1.22                       100.0\n",
    "```\n",
    "每个特征值解释的变化比例在第二列中给出。例如，4.124除以10等于0.4124，或者，通过该第一特征值解释约41.24％的变化。解释的累积百分比是通过将所解释的变化的连续比例相加以获得运行总计来获得的。例如，41.242％加上18.385％等于59.627％，依此类推。因此，大约59.627％的变化由前两个特征值一起解释"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用函数**fviz_eig()**或**fviz_screeplot()** 生成scree图。结果如下：\n",
    "![4](https://gitee.com/luminious/article_picture_warehouse/raw/master/R-Study-Notes/Principal%20Component%20Methods%20in%20R/4.png)\n",
    "scree图能够表示各个主成分贡献率，进而决定选择多少主成分。目前没有广为接受的客观方法来决定有多少主成分就足够了，这取决于具体的应用领域和具体的数据集。通过可以将主成分数量限制为占总方差的比例。例如上图，数据中包含的87％的信息（差异）由前五个主要组成部分保留，我们就选择前五个主成分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 实例  \n",
    "+ 变量分析\n",
    "+ 观测值分析\n",
    "+ 图表定制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1  变量分析  \n",
    "变量也就是样本的属性，在R语言中每列就是样本集的单个属性值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 变量基础作图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得数据\n",
    "library(\"FactoMineR\")\n",
    "library(\"factoextra\")\n",
    "data(decathlon2)\n",
    "decathlon2.active <- decathlon2[1:23, 1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Principal Component Analysis Results for variables\n",
       " ===================================================\n",
       "  Name       Description                                    \n",
       "1 \"$coord\"   \"Coordinates for the variables\"                \n",
       "2 \"$cor\"     \"Correlations between variables and dimensions\"\n",
       "3 \"$cos2\"    \"Cos2 for the variables\"                       \n",
       "4 \"$contrib\" \"contributions of the variables\"               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA计算\n",
    "res.pca <- PCA(decathlon2.active, graph = FALSE)\n",
    "# 提取变量的分析结果\n",
    "var <- get_pca_var(res.pca)\n",
    "var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "factoextra包自带了提取变量的分析结果get_pca_var函数，其中：\n",
    "+ coord表示用于创建散点图的变量坐标。coord实际上就是成分载荷，指观测变量与主成分的相关系数\n",
    "+ cor表示相关系数\n",
    "+ cos2表示因子质量，var.cos2 = var.coord * var.coord\n",
    "+ contrib表示包含变量对主成分的贡献(百分比)\n",
    "\n",
    "接下来我们将介绍如何可视化这些变量并得出有关其相关性的结论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**相关曲线作图**  \n",
    "一个变量和一个主成分之间的关系的代表着在PC坐标系里面该变量的坐标，对变量作图我们可以用fviz_pca_var函数。  \n",
    "这张图也可以称为变量相关图，它展示了变量组内包括和主成分之间的关系，正相关的变量是彼此靠近的，负相关的变量师南辕北辙的，而从中心点到变量的长度则代表着变量在这个维度所占的比例（也可以理解为质量，quality）。这个图根据coord获得。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 4 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Dim.1</th><th scope=col>Dim.2</th><th scope=col>Dim.3</th><th scope=col>Dim.4</th><th scope=col>Dim.5</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>X100m</th><td>-0.8506257</td><td>-0.17939806</td><td> 0.3015564</td><td> 0.0335732</td><td>-0.1944440</td></tr>\n",
       "\t<tr><th scope=row>Long.jump</th><td> 0.7941806</td><td> 0.28085695</td><td>-0.1905465</td><td>-0.1153896</td><td> 0.2331567</td></tr>\n",
       "\t<tr><th scope=row>Shot.put</th><td> 0.7339127</td><td> 0.08540412</td><td> 0.5175978</td><td> 0.1284684</td><td>-0.2488129</td></tr>\n",
       "\t<tr><th scope=row>High.jump</th><td> 0.6100840</td><td>-0.46521415</td><td> 0.3300852</td><td> 0.1445501</td><td> 0.4027002</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 4 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Dim.1 & Dim.2 & Dim.3 & Dim.4 & Dim.5\\\\\n",
       "\\hline\n",
       "\tX100m & -0.8506257 & -0.17939806 &  0.3015564 &  0.0335732 & -0.1944440\\\\\n",
       "\tLong.jump &  0.7941806 &  0.28085695 & -0.1905465 & -0.1153896 &  0.2331567\\\\\n",
       "\tShot.put &  0.7339127 &  0.08540412 &  0.5175978 &  0.1284684 & -0.2488129\\\\\n",
       "\tHigh.jump &  0.6100840 & -0.46521415 &  0.3300852 &  0.1445501 &  0.4027002\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 4 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Dim.1 | Dim.2 | Dim.3 | Dim.4 | Dim.5 |\n",
       "|---|---|---|---|---|---|\n",
       "| X100m | -0.8506257 | -0.17939806 |  0.3015564 |  0.0335732 | -0.1944440 |\n",
       "| Long.jump |  0.7941806 |  0.28085695 | -0.1905465 | -0.1153896 |  0.2331567 |\n",
       "| Shot.put |  0.7339127 |  0.08540412 |  0.5175978 |  0.1284684 | -0.2488129 |\n",
       "| High.jump |  0.6100840 | -0.46521415 |  0.3300852 |  0.1445501 |  0.4027002 |\n",
       "\n"
      ],
      "text/plain": [
       "          Dim.1      Dim.2       Dim.3      Dim.4      Dim.5     \n",
       "X100m     -0.8506257 -0.17939806  0.3015564  0.0335732 -0.1944440\n",
       "Long.jump  0.7941806  0.28085695 -0.1905465 -0.1153896  0.2331567\n",
       "Shot.put   0.7339127  0.08540412  0.5175978  0.1284684 -0.2488129\n",
       "High.jump  0.6100840 -0.46521415  0.3300852  0.1445501  0.4027002"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gFpahKuSKnPKgAkak9iLZr6VgKKSKchUOQv1tVYAc7NASRqT2KtmPpVBaYgLYyp\n+5subIbD1YI7+nH3TAASLUex57MkPg1PD1KUC+31B0dQCiARe1Jre2JYAFNTkYO0eG2Gr+lk\nE4wn1I8FkKi1berYwyOJVG2+tHZb/D5lkZI/OGIkgETsSa1dUxeU+IG08m2yIg3x4IiRABKx\nJ7UMTO2bJXYgrX2ZpkgnBXRckD73S1UWl/vHyXhRdYzEHKVMUJLnaaqNVz5OmkgO5uKdnKJT\nEJBuqtfNLaoFZQaSZbPEDKT1r3oHGvTBESFNf5w910rzVRS357taeD9vqni5BjZRdiCVNs0S\nL5A2vukXKM1doD+3k1LnR2u4tMLftumf3eY6kYP0LB7aq0fxdDKfKUeQzFFiBdLWFz0CpXpw\nxLtoe0PFZ6V8L9u3abm4FQw5SNed1476R88RA5BMe3hEkarVF64mU7kGSvjgiIu6Vv2h97k+\nrlh0VdsgOZ7so+/ahVGuIJVGmWME0vbXnALd+zGxM+3MPvX/GYB0L9Soj+erjEEyaJbYgEQf\nqEGTbGd6Um/NvKyO09shr1vRLjX9vta0+VjdPmW/Y9Xf7mNrBQLp3sRzdzJeVNYglXs1lAtI\nxHXe8OffzvSpins/XqDUpR88Pjc18jwBqamnRckXpOK3rMcdnIwXlTtI23WKKlK1suzw9WVZ\nDrWYxWC5969TfRLzt93C+VP9qFf18KHOf+XfWT30rl3F0PjN1e6ggX6IQTo1w3Sq/vtLekEj\nOUfMQKIbt9rYwsqy/bdXZB5oyAdHvH+vRd34VBt5ly0aJ1W3Un/qNAbpNX6TE0hKnX77c7Kk\nXbv8QSpXs8gCJMJeWIQHRzzUdQGP72J3jDR+kxNIn4qh4vGpBxsIOToKSCt1jG4u6OKi7VfX\nZRKo9TGI26m5RTwEgVSjVI+PEM6zq3UYkMqlVDIAiehwxuVI3mrvL6odLP6M8Wi7dq9p1659\n89y9+eYFUllPaOhGFcl0JJDm1S09SDRTMCJcjfVS6lHVvJd2Qrb+2w823OuXn7IHaRiBKNRv\n+Tm3ILlVXHKQ2jNIvyelLm6zlhZ1LJDKSZ1LDpLpyluBRnpwRD9luh3vVqdb1zb1w98VM327\n1K107r91qd9sP7YXNUjDGaTnifY2ZAcDaYQSoenCkk0kO9qYL+BeGSz3/u9aNJNW+0sQipaZ\n/oRsXTGL4XDo0s8bqD6+Nm+2H9uLGiTtDNLr4mS8qAOCpNW+tCBZILAcaJoHR7QbfV+XudAG\nG4j0Qzz8TX0GqdUhQSr1I2Miv4Ul46/sayHQZA+O6Ld7Vr8rpqxBIj+D1OqoILUVMSVIVpVt\nGijJnR09QXqpS/fiUV+o1EwY+GfP5tsAACAASURBVNz+R51ezYygYdXvp24iBqmkPoPU6rgg\nlbS/nGq2YLi+kcZ7TxS37/TF/pCoPXxvhse7a5Z0kLRP3UQNUhgdGqT6eZpUVpYguZ87Tf7g\niNmp2ea00W89JndX5/+r+k6TqUH9p44CSCJMqeqlHUi22+z3nsODI2YgFeryaM8RndT7n77S\n+FNHBQKJ9kCu/EfPkSyQSqIitQLJeoNNoEweHNFH8Wm7dko967Hw/l4OM5C+nzoKIMkxpaii\navL/xpous3n4PDiiD+TZT19tT7nWdxFZAqlasfvUVT/Es781uQc1FUDq5F2oxiA5bCgARSXB\n8PdzeNHVyknX7t2v+vfw2QFakM4AKbCpZ7magmS9Edpxek2+J2RP3YuT+u1q5W0YbPjOsTvV\np5v++IBUPlQzuQFdu4CmXoWrRv/trGXuSnzmWJMzSN0Uofe4n1Tf16od/n7qc+x+v586ihik\n8u+kLh+AFNbUo3iNQLKzDzCXSZMnSN/zRc9zP1fgc1Xq3Ny8dJhj13x69jkDSg1SPUxfPAFS\nYFPnHp4JSDbWeiA8inT90OL7VoBI6UEq/wryIVCANJdbGRuAZO47yXPaIt09NNc+kQFSWV4B\nUgxTp98rpf3dWMHAaLb1JEVqPLalrxAiUrc6GnlmA0BakT1KeyAZTh5a2nDUvbcdGw7edoYC\nCcPfcUxtm6VtkMzMVtaKsfc0J1cAEqEnvWUqU6ui3gTJaObQ6uZC7j0BP2GPkdC1y8HUpoKp\n4c/yRzsb2lgnwN6Tnt0POmoHkPIwNa9r6yDtTnfY2QblHSsGfkiLtI8fIBF60lsmNjVEaRWk\nnZO0+/b+e7/Q/tAWaWfNH6Tnqb6091bU9wyjE0Ayk1GztAbS5qklI0id936rA0ddpM1G2IP0\nqkvj1jxNQ1He1w4gmcqgxqtyKX9bZ5YM+x/OkxBITU02xx6ks3o1d7usZ69S3o5rIchrc2u/\nepPdTMP7N6Z+8XNdv3/yP4MK0l9daR6oxbqhTPf3qlzI3+qXLA70zfugFiMIHkWqJure9DNd\nFSlITZT65VI0WmyRTs0MxHt/nb02v29YPDXlt3zHv2xB2q37SyCt5pNw791G4Cz2fhGc6Sq2\npsYSC9Kfapu/tgv5+y24YfHZ3N351DwHZ+6ZL0jlTtRqmr/lta0HnVcnBfqMYG/svQE402/s\nmzorUNfuN3TXrrn26VN0d1C6qFtfkN/Fa4PQU90WPbMGaRODKUjLP932FX8cKNEpIN3UGpyx\nBJ1HijvYUB0edQdKZdXN64vpu3huHi3w7h7ccW/uuHdTHVctSLf+Gb1l2XPzVzT3VS+q9b4g\nPU5G98ZgBFK5/hswAWlh4MGt9v/rv0zAz8iKxlLUzIZu+Jv2YRRrMVbtUfEdSdDKWT+u7Plo\nbgHYXNzVktSA1D+tVwepeSRIc3R1GUAaHua7I14grRExBml2uOR4fQZBbV8Ch1uRrkrCCdm1\nGC96/3EHpPOn6gt2f+t3/3Vv3vtbcfYr1pj1D9TpQHrUK36a+2hsi2HWl2q2Wr95sS0IeqV3\nvwZvg0GGRboswSDVNyP71u0dkNoH836Gt/993xyDVL937p7m1n3U9hE/+4d9LLM+r6AaSOOD\nJYurEhbq/n6gDl01lkW6JLkgVT27l9a32wHp+4YG0vzj0ZBj/4Zx5plmfRr5FyS1vtKak03j\nQXCQw7RI55IL0rlqIS7fA5c5SH1Dcj46SOWkWRpA+jZMO/tmVAD/iEcHOtMAYg8SdSl2Whv9\nLutbK/WDaXOQbt3w99UMJO1pvOfmm3/9GyfTJ4tyzrqaF5CafzL7zmYuA4AzFeciHYkUpNtq\nqRaVlpaNtHo+tvyekV0A6dkNGTx3QdKfxlu/V0E6GWx4G816Yp71oYiahX73l0Yj1sDYBIf5\n3gc2pe3a3ZanEZTF8Ge8bKb1GULaHKE5SN2dAE/lAkjjbtz3ToFDr7DSdTL8Xd9scFvss673\nWbXXw6dTQKyaHPZ7H9SU+BjpukwIOUjDqdhh1uoCSO960ur1O1K3CtL3ToH9VycnZG+niqtd\njiRkXXU7pb6NkYaJV19NwN4HNKUebCgWJ+TQt0i+EpMgetMaoxV5+crY+1CmcUbtVkAaEviv\nEv4P+f8yOOnjyub/n731QoJkKLRIRlpmpZO+ztJJ2piBZmlK2iJN7+Y/vAZIRKYGrKxK5ykE\nSkKLlEikID1HU6Qf3yegASRzmTQsTp766zLASXW+RRrDlPYY6VUUt2c9uvV+3lTxHQoHSCNZ\nseIZ6aKnGv4QSkydFwCSfk5WH75jCFKIwwQtQXTtiuddCzY8iYtATJ0XAVL5uddX+Vzu41k1\n/WyGQls2VgCQKOpQiD7YgpyvkN0IovckJUlMnZcBUgDRg2T8wFM7Vnhk3QDkwZMykTz2PpXp\nEUGqq9k/7RVZw5I866Yx63tvHdC+KaHEmB4QpGCdsPQjGKae0xE8Eomp8wCJSorXs+y9Ta1/\nENTqCw9lVaTWOiRIgU7tp8i6W6M6/gZRYYip84cFKci9vwMPf0cw9eiYTr9GUhZi6jxAovQM\nsRPxsu55cDfzpCgLMXVeAEivkzq1s4RIz6TgaRSaKEZIFu5T4um4aEogMab0d1ptL7kDSAFM\nyUYZFwL1NxZZpGQivvf3rZ65WpMEkIhNSUfql7uLIUx9JcaU/mkU5bs4AyRKU+rTXWv589yG\npCKlVwCQKpIuAIlG9Ay1tmtb8zEVUqSBTGkv7OumfP+pM0DyVc9QiEhXk0M6FEghMaakIL37\nn89f0t/R44Gkt0NRQfIhiXeRhjalHf5+97fjep0BkptmnbkQkW54uv8Csi3SKKYHPiHLznT5\ngCgySO7J5Vik8UwBEg/T9UGF6CC5ZpdbkcY1BUjJTXdG5uKD5Ni9Y1SkCUypQfpc6a/zyRck\nk4KKO9hgusKCeBRpKlNqkC4BLpjLEiTjMkoCkgtJyYs0pemPY29k/RP16xrLqrIDyep3Jg1I\nDt07KXVeBkinEJf5ZASSfWOdCCT7RklKnZcB0vt0M3y8nbkyAcmxw5tgsKGTtFu/pDQlB6mZ\n1IBjpIl8CiQdSJYkSanzMkDCYMNUvmWRECS7AyUpdV4GSBhs0ETyg5ISJKtGSUqdlwHSBYMN\njega5WSDDbarSqnzIUx/XE/Gr390MXjWqqX+Bbghl5iLWVODZN69E1Lng5jSgxTiDqaCQApw\nIV5qkIzXFlLng5gCJDoFupiVAUiGqwup80FMA3TtAog9SCEvZk082NDK8s78hBJiKheka3vX\nr/KsugfV3ruYPtdCFe1JYW1xwZNIoS9mZQGSUcbF7D1/kJSK17U7qXtZ43NqXz77DZ6ajReT\nxQVPf0W5mJUJSAYkidl7gKTrT6lXfUfKv+bVcJOIpzr/lX+n6jN9ccnTS/EuZuUC0n73Tsze\n05v+OF+LRhzItpaPkR6q+BSqvUHyRd26PF8bbp717Yy0xSVPZ8W9mJXDYIPZt8TsPUAaqzo8\n6g6Uyqqb11Xus6qPid71B9pi9eFdFffmWdG3ztNFCS5mZQTSztfE7L0EkD73c1XVLg/nkJa0\nAlLVHhXfkYSugmv/jRbvNQLPOriWJOv9TnUxKyeQtr8nZu8FgPQsugOkYvHAxFFrw98XddFC\n2gbp/Kn6gt3f1tNcSS9mZQXS5oGSmL3nD9JbqWt98P+6KEV4VdIKSM+6jfmGtA3Su1n6DGuY\nz39OfDErm8GGTuulIWbv+YN060/qDDcvJtEySFXP7qX17XZA+n5mDBKPi1m5gbROkpi95w9S\nofoJq+/+/A6F/i3eo+Vcdewu/WjDgE07wvD5DjZ81NkeJEYXs7IDabV7J2bv+YOkFTHx/Oc5\nSI/mTGs//j1s8NaNeV9HizYgMbuYlR9Ia+kXs/fkpj/OnulBas7Hlt8zsv0G27Ow5/rgSVs0\nBMn3RLKIrNfyTc7i98XsPUDS1M4Q0uYI9Rtsxw1P48V9kNhezMoSpMX0itl7gPTVcCp2mLXa\nb/Bdz1S9fsaLmyDxvpiVJ0hLFmL2XgJImtzjmqqOkfpCim6/+V/MyhWkuYeYvQdIhBJzMSvH\nwYZW09ITs/f8QQokcpAkXczKF6RpNRCz9wCJQOIuZuUM0rgeiNl7atMfd8/4ILmQNI1S5MWs\nrEEade/E7D1AspMepNyLWdkONsysxOw9QLLRwI3wi1m5g1Sikc8apH7CKiaGLYk0f72ZmL0H\nSOZS2VzMyh+kvvEXs/fHBsmKpIwuZuU92NDJ+lpJY0kwzRikfZQkJCicKbnnkc8o/Hh48gep\n3GFJQIICmtJ7VmUtZu8BkrVwXXQ8TyVm7wESuSexxJgGyV8QUwFFKg0kWpIEJCigaZg6H2L+\nooAiBUjkEmMaqvGg9+VfpD8+ngBJtmmwXhi5Mf8iBUj0EmMaLtDjXeIlDyRSkvgnKKRpyEBp\n6wb/IgVI9BJjGjRQ0srBvkj7SgmQjmgaNlDK2sG+SAFSAIkxDXzKh/BAiX2RAqQAEmMa/Nwp\n2QbYF6lEkChJYp+goKbhJyFQbYF7kQ5VEiARSoxphNk8B7nnJkCisxJoGiVQkkrCvUgBEp2V\nQNM4gVLUEu5FCpDorASaRgqUoHvHvEi/NVIUSIQkMU9QYNNogeb+gA+AxDxBgU3jXTrkuyXm\nRQqQmCcosGnEa/A8u3fMixQgMU9QYNOoF7N6bYx3kWr1URZIdCTxTlBo07hXhef7NFFWIP2z\n0Y/V2hAPKZU6gkCyqY7BQTJA7buIFomt56apc4VhXaR6bUzfIu0LIEnw3DZ1rTGsi1QwSGQk\nsU5QcNMEgTpWGdZFCpCYJyi4aYpb0LmNg7MuUoDEPEHBTdPcy9Flq5yLdFQXpYFERRLnBIU3\nTXRTVIfNci5SgDT1pJIY01R3F7bv3nEuUoA09aSSGNN0gdrWHM5FCpCmnlQSY5owUMuqw7hI\nxzVRHEhEJDFOUATTlIHade8YFylAmnsSSYxp2kBtag/jIgVIc08iiTFN/Cgji80zLlLpINGQ\nxDhBEUxTPxPMvHvHt0gn1RAgEUqMaWqQzCPgW6QAacmTRmJM04NkGgLfIpUPEglJfBMUw5RD\noGZ1iEOkywJIi54kEmPKIlCjAyUWkS5pWgkBEqHEmDIJ1KAaMYl0rhxAoiCJbYKimHIJdL8e\ncYl0JoC04kkhMaYMBhta7Xbv2BYpQFrxpJAYUzYg7YbCtUhnVRAgEUqMKSOQdmLhWqR5gERA\nEtcExTHlBNJ2945rkQKkVU8CiTFlFuhGdWIWaa95BQRIhBJjyi3Q9frELdJOuYDkTxLTBEUy\nZRfoaveOXaSNFqofQCKUGFOGga5UKYaRlgBp29NfYkxZDTZ0Wo6JZ5HmA5I3STwTFMuUI0jL\n3TuWRbpU+QASocSYsgRpMSyWRQqQtj29JcaUKUgLcXEs0sW6JxUkX5I4JiieKdtAZxWLY6QA\nac/TV2JM+QY6PVDiGClA2vP0lRhTzoGO6xbDSJdrnliQPElimKCIpqwDHVUuhpECpH1PT4kx\n5TrY0Erv3vEr0pV6B5AIJcaUN0h6fPyKFCAZeHpKjCl3kL4B8ivS7EDyI4lfgmKasgdp6N6x\nK9K1WgeQCCXGVEKgKoRp6W0KkIw8/STGVESgKoRp6Wm6WukEg+RFErcExTWVEWjdveMWKUAy\n9PSSGFMpgSp2kQIkQ08viTHlP9jQSTEr0vUqJxkkH5KYJSiyqRiQuEUKkIw9fSTGlFn13DK1\ne+SsoanrFzcqnGiQPEgSU+ePDlKIYAHSVACJj2c4U/LaBpBmciZJVlWS4Blw+LtemHTxtl/u\nyDXSrdoGkAglxlRGoBpICiARaCtGV5JkVKVQpjIC1VukaY3zGoNwjHSzrgEkQokxlTHYYNG1\nsxNAWpAjSWLqPEDqQaqDvhXq/Oq4uqnipq/astUt3VVxr1ZX6lZ2K6vbxzNSgGTr6SoxpiJB\nqqI+q1b1y0u9cNNW1UG61x8+z90q3evCL9LtmiYeJEeSxNT5Q4M0qAfk/Ff+nbrFT93sDKuO\nQao+fPR/69dF9b2zenhFCpDsPR0lxlRGoDOQzg0rXdfuXWpHSlOQ2g8/w+tXtfSnTj6R7tQz\n+SC5kSSjKoUylRHobLChG70bU1OWs7f0D7WBimFtgLQogMTCMw5IZXdKCSA5aC9GF5JkVKVQ\npjICnYF0Vn9lN363BtJ7GaT6ey919oh0r5IBJEKJMZUy2DAB6dEMNpxXQCrUb/mZffgdpPAb\nbDgCSC4kianzAElrb86jsYfub//yVn9yWQGp/qxvkFwi3a1iAIlQYkylgtSfkG1hmoBUf3hd\nO0a6qOIxGAOkNdmTJKbOHxikNdN6GHuyB9szhqaf2ke6X8EAEqHEmIoJdGyqqqOg8nNpp/0M\nb5bdEMO6AFIrgxitSRJalRh7RjC9t526opk019PRHh1dt0y8QTKoXgCJUGJMxQQ6MX2clToN\nk08Hkq7q9N4yAUitTGK0JUlqVeLrmcBUaX/JTBdkUrkAEqHEmMoebPiqHbUjNp3rSCDZkiSm\nzgOkTTmRZBmpUdUCSIQSY5oPSPWuBAbJrGZlA5IlSWLqPEDakbK/6hwgbQkgpfWMb9pPcLAm\nySpSw3qVD0h2JOVRlTh5puraKfsmCSBtCiAl9czU1LRWZQSSFUnJE5TUVEygyU2N6xRAIpQY\n05wGG8KaHhIkG5JSJyitKUAylHmNAkiEEmMKkAx1UJCC73cupgDJTMF/mQGSbFMxgQKkiYpK\n+nKxtfJUfI4NczEVE2ha0/CjV7YgFcMf7X9jASQJnvmZRjgxyRakwOfPcjEVEyhAGksHyZoj\ngEQuDDYYKMbkTS+QvodIzTypf7T6IfbLUtSFnqWIaxI9SNqymfjMes/FFC3SvqJc4OZ1jDRd\n3hVAohZA2hczkNp+XEyQQl4ZnIupmEDTmca5CQjnrl3Ie1XkYiom0GSmke7v5guS3cgdQJLg\nmZcpU5CGmQ2Ftmws6xiD3c8vF1MxgaYyjXUPbK5z7XqFulVzLqYYbNgRQGoFkLYFkLYV7TlB\n3EEK9TibXEwB0qbiPQKSPUiBnlSYiylA2hRA+gogxfbMx9SFo2xBCvM09x2JMRUTKECilFuM\n28WRTdbZeGZj6sQRQKKUGFMxgQIkSjkW5maB5JJ1N2GwYV1uHOUM0maRZJJ1RwGkVTlyBJAo\nJcYUIK0KIC1oo1DyyLqrANKaXDnKG6SNYski66w8szB15gggUUqMqZhAI5u6c5Q5SOslk0HW\nmXnmYAqQVrVWNBlknZlnBqYeHAEkSokxxWDDknw4yh6ktdIRn3UvAaQFeXGUP0gr5SM9634C\nSAsCSNsCSHMBpLn8ODoASMslJDzrDD2Fm3pydASQFstIdtY5eso29eUIIFFKjKmYQAESpbwL\nc6GURGedpadoU2+OjgHSQjlJzrq/MNgwlj9HAIlSYkwB0kgEHB0EpHlRyc06hQDSSADJXNOy\nkpt1CgEkXRQcHQakaWmJzTpbT7GmJBwdB6RJeUnNOl9PqaY0HAEkSokxFRMoQKIUUWGOSkxo\n1hl7CjUl4uhIII3KTGbWqYTBhl5UHB0KJL3URGadTACpExlHxwJJKzeJWacTQOoEkBw1FJzE\nrNMJILWi4wggUUqMqZhAw5oScnQ0kIayk5d17p7yTCk5OhxIfemJyzp7T3mmAMlLPwE8O4kx\nFRNoSFNSjg4IUluA0rJOKww2UHN0RJCaIhSWdWIBJGqODglSXYiysk4tgASQKASQAnjK2fva\nlJqjY4JUFaOorIvwFGVKztFBQSp/JGVdhqckU3qOjgpSgJ+kUlBVEhNoGNMQyT8qSCF+lORU\nJTGBhvkR5VOkGYAU4mdJTFU69GBDmAPk44LE5oAzgemRQQo0ZAuQaE3pBZBIFeps/IFB4nIu\nIYHpcUEKNj/syCAxObudwFRMoEHmWTIq0kxA4jFxMYGpmECDzPxnVKS5gMRiKn0CUzGB0pqG\nvKrz4CBxuLgrgamYQElNg95nACAFMCUVBhuIFPYWUkcHicENMBKYHhGkwDc1PDxI6W/JlMD0\ngCCFvs0uQEp+k8AEpgcEKbQpQEp+/+cEpmICJTMN/gQFgFSmfiJBAlMxgVKZhn+mD0CqlfQZ\nOQlMxQRKZBrhcY0AqVHKxx8mMBUTKI1pjAcIA6RWP+mebJ3A9FiDDVEeaQ+QehGQxLYqTXUo\nkOaJBUhBPf1J4lqVZjoSSAtpBUhhPb1JYlqV5joQSEtJBUiBPX1J4lmVInnyNF1MKZ8izRQk\nX5JYVqVYnixNlxPKp0hzBcmTJI5VKZonR9OVdPIp0mxB8iOJYVWK58nQdC2ZfIo0X5C8SOJX\nlVZ0jMGG1VQCpBieHiSxq0prOgRI64kESFE83Sc5cKtKqzoCSBtZBEiRPF1JYlaV1nUAkLZy\nCJBieTqSxKsqRfZkZbrdq+BTpJQg/eOon9QBQD7imL7gIBmglsDTqU3i9Jsc3ZOR6V7y+BRp\n/iA5kcSnKiXw5GO6mzo+RXoAkFxIYlOV9pTzYIPBoCtAiuppTxKTqrSvjEEyyRpAiutpTRKP\nqmSgfEEyyhlAiuxpe2qWRVUyUbYgmSUMIEX3tCOJQ1VK5snA1PSHj0+RHgYkO5LSV6WEnulN\njXPFp0iPA5IVScmrUkrP5KbmmeJTpAcCyYak1FUpqWdiU5vjWT5FeiSQEv/SYbDBSMm74ABp\nX8Y/dgAplWn6QSGAZKKEo0EAyUAMTlMAJCOZNUoAKYmp9ZWYDCpUp8OBlG7qCY+952zKYyoX\nQDKVQb7E1E8xgcqZXAyQjLXfgxBTP8UEum/K5XIXgGShFBeM8dl7jqZuN6rhU6QHBWkvb2Lq\nZy6DDYzurQGQ7BT9phoAaV2c7vYEkCy11SgBpKimrO4/CJCsFfcOngBpRR4PK+VToQ4NUtx7\nSrPbeyam3O7RDpActPZjKKZ+igl01ZTdU0MAkpPiPcCK494nN/V8Bj2fIj08SMupFFM/xQS6\naOqJEaciBUjRnpaNwYapeD59HiA5a/7DCJDCm3o3R0umBAJIHprmFCAFNyXAiFOFAkitJj+P\nACmwKUVzNDOlEUDy0yizYkASE+jIlAgjTkUKkAbpyRVTP8UEqptSYcSpSAHSV9rvpJj6KSbQ\nrylZc1RyKlKApGtIsZj6KSbQwZQQI05FCpBG6n8txdRPaYMNlM1RyalCAaSJfkKYlsFMZYFE\njBGnCgWQpmqSDZBCmFJjxKlCAaS5KpQAEr0peXNUcqpQAGlJIXIuZ+/DmGZepABp2TTvn8/4\npj8/UiIFSMSm9ChJ2nta1WUpI1JnT4C0bkqNkqy9J9RPCNMylClAojelRemggw0/ws4oAKQQ\nptzns/AHqS9AgEQoPvttbkqH0hFB+jnKrCuAtG/Kec4/b5D0HyGARCg++21lmt1VaHFMf450\niRdAMjIlQUns3jtpWmR8IyXxBEiGpgQoCd57awm+nwxACmya0U3YApv+LBUVy0jpPAGShSnD\n24IyHGxYKSWARCg+++1o6oXSIUBaLSGARCg+++1syuwZJLxAWuzT+ZpuiU+FAkjWps4o5Q7S\ndsEAJELx2W8vU0eUMtn7ZW01Rs6mu+JTpADJydQJpWz2fi6D8mASaShPgORouvsL7GJqLxbV\n06goWEQazhMguZvaopTX3vcy/UVJH2lQT4DkY2rXLOU42GBeAACJUHz2m8zUgqX8QLL5HQFI\nhOKz35SmKTs3CUGyPEpMn6egngCJxDTZ4XYykKzHWljkKZwnQCIyNfh9zmjvHYb/meQplCdA\nojPdYymXvXcY+d83dROfIgVIpKabVSyLvXejaMfUWXyKFCARm25UNPl770zRlqmP+BQpQKI3\nXatusgcbfnwoWjP1FZ8KBZCCmEa7RjQSSJ4QLZsSiE+FAkihTOPctSAGSAQUzU1pxKdCAaRw\nptMKKBIkGopKznki8QRIQU1HBxby9p6MopJ5nvw9AVJw0wEmYXtPSVEpIE9+ngApimkDk6S9\nJ6aoFJInd0+AFM3Ud/x4UWIClZMngCTAlLyOkucvVNMpxxQgCTH9oaSJNn9dYOKKlIEnQEpj\nSgUTWf50vGUWaVpPgJTOlAImgvz9zJpIuUWazhMgpTVNOoNtjhCB6ZrEmAIkqaY+MDkGuoaQ\nl+m2xJhGA6n4LlWy+iqf/eZm+vOzXbddPDc2RGxqIjGmsUD6slMMf0zFZ7+5mv58ReY5siY2\nNZcY00ggFSVAimNqxJRR/mwbOw57n840ftcOIMUyXWdKLa81VrxAczBNCpKq9Q8KrwkjSltO\nHdpRFBIkQ/H5AcnFNPUtizM0RdfuiKZiApVjGhqkYawbIDEyFROoHFO0SEc0FROoHFOAdERT\nMYHKMY0MUv0XMxuSm2KwgYkn5trJNgVITDwBkmxTgMTEEyDJNgVITDwBkmxTMYHKMQVIRzQV\nE6gcU4B0RFMxgcoxBUhHNBUTqBxTgHREUww2MPEESLJNARITT4Ak2xQgMfEESLJNARITT4Ak\n21RMoHJMAdIRTcUEKscUIB3RVEygckwB0hFNxQQqxxQgHdEUgw1MPAGSbFOAxMQTIMk2BUhM\nPAGSbFOAxMQTIMk2FROoHFOAdERTMYHKMQVIRzQVE6gcU4B0RFMxgcoxBUhHNMVgAxNPgCTb\nFCAx8QRIsk0BEhNPgCTbFCAx8YwLUtyteQiBkktMpG6BAqRFIVByiYkUIBEKgZJLTKQAiVAI\nlFxiIpUAEgRlKoAEQQQCSBBEIIAEQQQCSBBEIIAEQQSKB9L3+efWD0OPKT24gm+k0zBTxrIt\nIQVay6eKRgNJK83hD0ONguMa5DhM1uUppUBreVXRWCAVJUAiFECil18VTdC1Y534WQ3lKZkg\nsQ2yE0Ci0yjvfHv0QkHiW6CNABKdhNRQIWGWkiIFSJSaBcczUDnVU0iBNmIN0tCacwepDVRI\n3gFSCLEGaRB3kFoJqaFCmpRsmgAAAoxJREFUwiwlRQqQKDXNO9M45VRPIQXaSBJITVlyHrvp\ng2MeqJAwS0mRelVRzLWDIAIBJAgiEECCIAIBJAgiEECCIAIBJAgiEECCIAIBJAgiEECCIAIB\nJC5SjYrbu32xtMpt9O7z2vx3Vksfv69KnV/tu8W9+u+jTvWr65M2aqgTQOIi1etZroB0G737\nauewPIY39Y/frVVF0l09f9W9/rRFqHgFif7wAkhc1GJQtSTFZ3mFqo3Rs1U86r9/w5ujj6/q\nVrNTNUJF9aYq+gapAo/zZDfBAkhc9AXi3r6o/l3UpXyf1KVGqyheOki3FohT0b05/rhQvWX3\n76Z++49uwXfliAJIXNRj8KfOffW/VI3M76n6Ux8N3UYdvo9qeLir3+7N20J/UGuRvpOZb2ql\nxYN8BJC4aMDg245cywqTW9mzopNybw55/qoW6/vmFKSnejSoVf8/1OP77j3YPhxYAImLFkB6\n138+3480Ui71h1U781kH6V2c6//u9ahdtWI3ele+K/ggcgEkLloAqdT/lCNSmsVr3SytgdRx\n1OhX3frRu7WhdchPKFQu6uv3q24xzEAaRsxnH1e9Po2j8qQ+3bHSdDWISChULurr96U+nPEG\n6VfvwD2rA63BESAFEQqVi77nkUoDkNpjpPGb2scvpbVHdYNUDi0SjpGCCCBx0dC8vEoDkO7q\nWU7f7Jbq/856U9V0FtvRuxKjdoEEkLiorfmn26d9sQNSdx6pXAFp1Oe7qL+yG70rcR4pkACS\nUN1cM6cwsyGEAJJUFY/9dRaEuXZhBJCk6uVGBGZ/hxFAEqvueiQ74XqkQAJIEEQggARBBAJI\nEEQggARBBAJIEEQggARBBAJIEEQggARBBPp/0LE2kFgibgoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coordinates of variables\n",
    "head(var$coord, 4)\n",
    "# col.var设定线条颜色\n",
    "fviz_pca_var(res.pca, col.var = \"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**代表质量作图**  \n",
    "变量在PCA结果里面的质量（quality）称为cos2，可以使用corrplot包在所有维度上可视化变量的cos2。 或者使用factoextra包的fviz_cos2()可视化，只是显示效果相比前种方法弱。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Dim.1</th><th scope=col>Dim.2</th><th scope=col>Dim.3</th><th scope=col>Dim.4</th><th scope=col>Dim.5</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>X100m</th><td>0.7235641</td><td>0.0321836641</td><td>0.09093628</td><td>0.0011271597</td><td>0.03780845</td></tr>\n",
       "\t<tr><th scope=row>Long.jump</th><td>0.6307229</td><td>0.0788806285</td><td>0.03630798</td><td>0.0133147506</td><td>0.05436203</td></tr>\n",
       "\t<tr><th scope=row>Shot.put</th><td>0.5386279</td><td>0.0072938636</td><td>0.26790749</td><td>0.0165041211</td><td>0.06190783</td></tr>\n",
       "\t<tr><th scope=row>High.jump</th><td>0.3722025</td><td>0.2164242070</td><td>0.10895622</td><td>0.0208947375</td><td>0.16216747</td></tr>\n",
       "\t<tr><th scope=row>X400m</th><td>0.4922473</td><td>0.0842034209</td><td>0.08039091</td><td>0.1856106269</td><td>0.01079698</td></tr>\n",
       "\t<tr><th scope=row>X110m.hurdle</th><td>0.5838873</td><td>0.0006121077</td><td>0.20149984</td><td>0.0002854712</td><td>0.05027463</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Dim.1 & Dim.2 & Dim.3 & Dim.4 & Dim.5\\\\\n",
       "\\hline\n",
       "\tX100m & 0.7235641 & 0.0321836641 & 0.09093628 & 0.0011271597 & 0.03780845\\\\\n",
       "\tLong.jump & 0.6307229 & 0.0788806285 & 0.03630798 & 0.0133147506 & 0.05436203\\\\\n",
       "\tShot.put & 0.5386279 & 0.0072938636 & 0.26790749 & 0.0165041211 & 0.06190783\\\\\n",
       "\tHigh.jump & 0.3722025 & 0.2164242070 & 0.10895622 & 0.0208947375 & 0.16216747\\\\\n",
       "\tX400m & 0.4922473 & 0.0842034209 & 0.08039091 & 0.1856106269 & 0.01079698\\\\\n",
       "\tX110m.hurdle & 0.5838873 & 0.0006121077 & 0.20149984 & 0.0002854712 & 0.05027463\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Dim.1 | Dim.2 | Dim.3 | Dim.4 | Dim.5 |\n",
       "|---|---|---|---|---|---|\n",
       "| X100m | 0.7235641 | 0.0321836641 | 0.09093628 | 0.0011271597 | 0.03780845 |\n",
       "| Long.jump | 0.6307229 | 0.0788806285 | 0.03630798 | 0.0133147506 | 0.05436203 |\n",
       "| Shot.put | 0.5386279 | 0.0072938636 | 0.26790749 | 0.0165041211 | 0.06190783 |\n",
       "| High.jump | 0.3722025 | 0.2164242070 | 0.10895622 | 0.0208947375 | 0.16216747 |\n",
       "| X400m | 0.4922473 | 0.0842034209 | 0.08039091 | 0.1856106269 | 0.01079698 |\n",
       "| X110m.hurdle | 0.5838873 | 0.0006121077 | 0.20149984 | 0.0002854712 | 0.05027463 |\n",
       "\n"
      ],
      "text/plain": [
       "             Dim.1     Dim.2        Dim.3      Dim.4        Dim.5     \n",
       "X100m        0.7235641 0.0321836641 0.09093628 0.0011271597 0.03780845\n",
       "Long.jump    0.6307229 0.0788806285 0.03630798 0.0133147506 0.05436203\n",
       "Shot.put     0.5386279 0.0072938636 0.26790749 0.0165041211 0.06190783\n",
       "High.jump    0.3722025 0.2164242070 0.10895622 0.0208947375 0.16216747\n",
       "X400m        0.4922473 0.0842034209 0.08039091 0.1856106269 0.01079698\n",
       "X110m.hurdle 0.5838873 0.0006121077 0.20149984 0.0002854712 0.05027463"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "corrplot 0.84 loaded\n"
     ]
    },
    {
     "data": {
      "image/png": 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8tVI4jECpGfMj2Por587sFZpI3cau6m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\nJAAIgEgAEACRACAAIgFAwP8HdraMxOUOtAgAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(var$cos2)\n",
    "library(\"corrplot\")\n",
    "# is.corr表示输入的矩阵不是相关系数矩阵\n",
    "corrplot(var$cos2, is.corr=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WSR2lKRZkhFKsgitaUizZCKVJBFaktF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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total cos2 of variables on Dim.1 and Dim.2\n",
    "# 在第一第二主成分是显示结果（通过值的叠加显示）\n",
    "fviz_cos2(res.pca, choice = \"var\", axes = 1:2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 一个较高的cos2值代表着这个变量对该主成分有较大的贡献值，这种情况下变量位于相关曲线图里面的靠近圆的边缘。\n",
    "+ 一个较低的cos2值代表着这个变量并没有很好的被主成分所代表，变量在相关曲线图里面就靠近圆心。\n",
    "+ cos2值就是为了衡量一个变量的有用程度，越高就代表着这个变量在主成分分析里面越重要。\n",
    "+ 对于给定变量，所有主成分上的cos2之和等于1。 \n",
    "\n",
    "可以使用参数通过cos2值对变量进行着色col.var = \"cos2\"。这会产生渐变色。在这种情况下，参数gradient.cols可用于提供自定义颜色。例如，gradient.cols = c(\"white\", \"blue\", \"red\")意味着：\n",
    "+ 具有低cos2值的变量将以“白色”着色\n",
    "+ 具有中等cos2值的变量将以“蓝色”着色\n",
    "+ 具有高cos2值的变量将以红色着色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kKwAwAA8AiCXVZRrgMAAJlDsAMAwOMYjfUPgh2A6crONDv2sQOASRHssodxWACA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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color by cos2 values: quality on the factor map\n",
    "fviz_pca_var(res.pca, col.var = \"cos2\",\n",
    "             gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), \n",
    "             repel = TRUE # Avoid text overlapping\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，我们也可以通过改变变量的透明度 alpha.var来说明其重要性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qcFTSgS4dbdbOW6ErMsImn93Kskm/R+enk0j0gsHk0jEke3JEqk+ul1U4lEskiL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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the transparency by cos2 values\n",
    "fviz_pca_var(res.pca, alpha.var = \"cos2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**变量对主成分的贡献作图**  \n",
    "变量在计算给定主成分变异性时的贡献以百分比表示。与PC1（即Dim.1）和PC2（即Dim.2）相关的变量在解释数据集的可变性时是最重要的。\n",
    "与任何PCA无关或与最后维度相关的变量是具有低贡献的变量，可能会被删除以简化整体分析。贡献值越大，变量对主成分的贡献越大。可以使用函数corrplot()[corrplot package]突出显示每个维度的最大贡献变量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 4 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Dim.1</th><th scope=col>Dim.2</th><th scope=col>Dim.3</th><th scope=col>Dim.4</th><th scope=col>Dim.5</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>X100m</th><td>17.544293</td><td> 1.7505098</td><td> 7.338659</td><td>0.1375524</td><td> 5.389252</td></tr>\n",
       "\t<tr><th scope=row>Long.jump</th><td>15.293168</td><td> 4.2904162</td><td> 2.930094</td><td>1.6248594</td><td> 7.748815</td></tr>\n",
       "\t<tr><th scope=row>Shot.put</th><td>13.060137</td><td> 0.3967224</td><td>21.620432</td><td>2.0140727</td><td> 8.824401</td></tr>\n",
       "\t<tr><th scope=row>High.jump</th><td> 9.024811</td><td>11.7715838</td><td> 8.792888</td><td>2.5498795</td><td>23.115504</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 4 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Dim.1 & Dim.2 & Dim.3 & Dim.4 & Dim.5\\\\\n",
       "\\hline\n",
       "\tX100m & 17.544293 &  1.7505098 &  7.338659 & 0.1375524 &  5.389252\\\\\n",
       "\tLong.jump & 15.293168 &  4.2904162 &  2.930094 & 1.6248594 &  7.748815\\\\\n",
       "\tShot.put & 13.060137 &  0.3967224 & 21.620432 & 2.0140727 &  8.824401\\\\\n",
       "\tHigh.jump &  9.024811 & 11.7715838 &  8.792888 & 2.5498795 & 23.115504\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 4 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Dim.1 | Dim.2 | Dim.3 | Dim.4 | Dim.5 |\n",
       "|---|---|---|---|---|---|\n",
       "| X100m | 17.544293 |  1.7505098 |  7.338659 | 0.1375524 |  5.389252 |\n",
       "| Long.jump | 15.293168 |  4.2904162 |  2.930094 | 1.6248594 |  7.748815 |\n",
       "| Shot.put | 13.060137 |  0.3967224 | 21.620432 | 2.0140727 |  8.824401 |\n",
       "| High.jump |  9.024811 | 11.7715838 |  8.792888 | 2.5498795 | 23.115504 |\n",
       "\n"
      ],
      "text/plain": [
       "          Dim.1     Dim.2      Dim.3     Dim.4     Dim.5    \n",
       "X100m     17.544293  1.7505098  7.338659 0.1375524  5.389252\n",
       "Long.jump 15.293168  4.2904162  2.930094 1.6248594  7.748815\n",
       "Shot.put  13.060137  0.3967224 21.620432 2.0140727  8.824401\n",
       "High.jump  9.024811 11.7715838  8.792888 2.5498795 23.115504"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pcGkIpL3r+ygX895Wx/G0Mqznl58CZ5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\n6P8D3f8N6tUET9wAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(var$contrib, 4)\n",
    "library(\"corrplot\")\n",
    "corrplot(var$contrib, is.corr=FALSE)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数fviz_contrib()[factoextra包]可用于绘制可变贡献的条形图。如果您的数据包含许多变量，则可以决定仅显示最常见的变量。下面的R代码显示了主要组件的前10个变量：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MMxGFhXFyHhGo7BwLq6CAnXcAwG1tVF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\nAYQEIICQAAQQEoAAQgIQ8P8CSeeYmRZ40wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NXdhfvZ7CLajHid3R5CspThWsR1cyz0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\nk4r08AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contributions of variables to PC1\n",
    "# 各变量对第一主成分的贡献\n",
    "fviz_contrib(res.pca, choice = \"var\", axes = 1, top = 10)\n",
    "# Contributions of variables to PC2\n",
    "# 各变量对第二主成分的贡献\n",
    "fviz_contrib(res.pca, choice = \"var\", axes = 2, top = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用以下R代码获得对PC1和PC2的总贡献。其中axes指示坐标，top指示画前多少个变量，红色虚线代表着平均贡献，高于平均值的可以被认为算是重要变量。如果变量的贡献是均匀的，则预期值将是1 /长度（变量）= 1/10 = 10％。对于给定的组件，贡献大于此截止值的变量可以被认为对组件的贡献很重要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AkLq9QyQpGwsEotIxQJC6vUMkKRsLBK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\nyP8DWvVFr/CNwmQAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_contrib(res.pca, choice = \"var\", axes = 1:2, top = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是注意给定变量的总贡献解释由两个主成分保留的变化，比如PC1和PC2不是简单的贡献率叠加，需要通过该公式计算得到contrib = [（C1 * Eig1）+（C2 * Eig2）] /（Eig1 + Eig2）。其中：\n",
    "+ C1和C2分别是变量在PC1和PC2上的贡献\n",
    "+ Eig1和Eig2分别是PC1和PC2的特征值。回想一下，特征值测量每个主成分保留的变化量。\n",
    "\n",
    "在这种情况下，预期平均贡献（截止）计算如下：如上所述，如果10个变量的贡献是均匀的，则给定PC上的预期平均贡献将是1/10 = 10％。PC1和PC2变量的预期平均贡献为：[（10 * Eig1）+（10 * Eig2）] /（Eig1 + Eig2）。可以看出，变量 X100m，Long.jump和Pole.vault 对第一主成分和第二主成分2的贡献最大。 \n",
    "类似于cos2的显示，最重要的（或贡献的）变量可以在相关图中突出显示如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zm8aa3wzO3jLzZPvvKdos+2z1p2e8hb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\nRAh2AACJEOwAABIh2AEAJEKwAwBIxP8D5xDpyDzsJIIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_var(res.pca, col.var = \"contrib\",\n",
    "             gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\")\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，还可以使用选项根据其contrib值更改变量的透明度alpha.var = \"contrib\"。例如，键入以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BzLqfwBTqVS7+IjA+xZJM+bHnfDqHRN4hCpOny0tEj0mw4LEemg3tXCSz/sLH/u\nejHAq0Vqvq0+MsC+RUqZuiVpItWHYRcVyePW3WJEaj1T3ZycM0TqvzEURC6CFfJFYumWZIlk\nXA27nEheFnXD6Hx4hsUlEo9HC7YKX5VYRfLdJOZZQUtVmadFqRiRBod2+vtBkUIP7cSL5KuS\nX8k6JnluktbZdYtUmW9n1ArzxFeki7qkz+5kg35jUKRj+j6q62AQRKoI99QyVpHaZ6nyVxmL\nRakYkV75VPehPf2t3yjMSdrfhp7+jkKk1KNbkiNS52xv5irjsigVI1L6PJbHYY0Dsmn59dYV\nKT2pk+UB2fdVW3e6DmvnTFwikbslVpF8Jhu6V01wVllzl2EOuEr2wRnmQUeki6oYPuzkSmwi\npbRuybNkbZM8ROpdfcRWZeZdhlnYtEiPJLnc827uflHJgyE+X0Mmj1bbkO7dkhCR+lfxMVVZ\n6y7DPGxZpHtyM767JXf/+ChFSp27JRkiDVwNy1Fl9Y4RRJrAFOncea/7PYFYRXJUiVUk4mTD\n4E2JvavMnF6ASBOsMWsXpUhOIzzfkrVMom2S4Zsz+BWsM0lnG/a+HJQ65qOb4dMAntNhT8vf\nkyNYpGuiWmM8H2IWKbXvlsKLNHKTE4+Cke8yXByaMY/JdDipybCTUwXIFema18Lw4Vtn9Bpy\neRRkaGHXLQUXaexmQdSC+dxl+JQfjnkZZwl0UNMiTV6I2kOuSMlnqucdeOKjFym16pa8S2aa\nRJhsGL3pFq1gfncZLkV4j97mgFWkvHkJE+mQT9Mp/fWTad9pCyJZqBRYpPGb1xEKNn72gmVY\nfvpnSSbFpbyUO70kxVI+4inC8rfV5Z0aJxAUb9sjUCSlDp/VMVkM7VrMjPDCijRxE0jXgk2e\nA2QZdlfJtZovKE5Py4/uH/N2deyIlO9IJOm2RHpnDiW3t55sYPJoMyKl091SUJGmbqbqVDCu\nuww/DvosM72HUFydfdWm3NTxqU9su5lDu6y9tV6cv+tVH4EiaZV098t0np1mQyJNdUv+JTNM\nchxVT96U2L5grHcZfn2eE935GNdpH5TupZ71BT+lSI/Wi5sRKdUnNJSDVha+GD0KLlI62i2F\nE2nm2bCWBVviLsM3dR7Qo16s9pHa729EpOII0mfWM5+cDoqNszWRRrqlYCLN3SPfqmC8dxlu\nXby9T5HqI0j3g+PKjLI9kdKhbimUSLPPmpgvmMMlRnZreVLF0fx3W49iaPfoDu2KF4/li69t\niGQcQXqceOI3KVJfJYaSNSbZTzbMP7NlpmBuF+rZreVDqVu2a/A4ltfjHC6lUsVkw1UvvmuR\n6hmIRH3q67hV+bYDH+JEYj2CVLBRkbojvDAiWTz7aKpgzpe7Wq5ldU1bZZK+cWk56Z1Pf2fO\nZP3SV3UX0/LF/KdO+sX8bQfkicR6BKlgsyKlrW4piEg2zxAbLdiSdxl+npP8pNX8oFB+CXfe\nyqoDsnrPIalEykaC5Ymd2dvFnf7zcwsAACAASURBVIHztx2QJ1LKeQSpYMsiGd0SR1htkqVI\nVs/iGy7YSncZrnZ2jupzJIxlV1ygSPxsW6S06pZYRbLbJHbPtBwq2Gp3Ga40eeib1Off3PT1\nFfmJaO+s1zo8ij4r7b3rBK9IfyP+HETyRXdL64tk+WzYXsF87gNEFSmtbsZTTAvns3rlpRam\nSMa7TjD3SH8bUmn+tKXRs3N5+GL0SKhIGX/9M0OIi0gzh2Eb2mvpeTctukiq+v+pZ7MOWprv\n73yfvHX8qHrXDfahXd8kVX8ZBSKxhHHcir8yab727R9Vbqyl/z3pvEVK1OlWTG0f1H+2PtR+\n1w3+faRep+QokjLhKdJeROK4FX8l0uxkg71H9VoGuctw1YjexdDu+/e7ngsvL0H/an0on62r\n33Xkg3+yoaOShQ3mR44QySvMUyVbkRw8KgoW6i7DVSO6V2fdZSqdMlnuwyJlHyzfdcRPpL+N\n0FqT1G0f6abykxswtKOGeXVLliK5eKQLFu4uw83097365vv35604Yag1tHtVHy3edWSFHkkX\nymUf6XlQpzdE8gnzUKk0aVokJ4/SH/g0oor0OufzB4U+n+n3/62XLsVkw8E8NUi/WzwZwhF2\nkXz3kTTXrG+FSF5h5G6pFGmy9l08yoZ0QausPkWouibps/j+f6bV9PfdPDWofNf9borcIvHM\n2j0Tth2kdJ8ipdRuyUIke4+KHSMBIh3yq9u+68mG73/5J6X+6X/p997/qtQxvyl2fWpQes86\npiPhzBpekYaOI9Gmv88QyT+MpFJh0kTt23ok5i7D3yuaV+hhY7CKNHhmA/E4EiO7FYk0wpsR\nyfIwrIC7DPf9qd9xD5tlgcmGLrQzG1K+ibsdi5S6d0uFSGOTDVYaEe8ybMV82Kg/9fsOYfZ8\nyD1pFSLxhLl1S5Mi2XhEvcuwHaNh4x1Q53M2YQQEi8TF3kVK3bql3KRhkeY9Gjruuuha2vpT\nf34qzIc9iMToUaQiuXRL4yLNeTRy9sIia+noj8Gvv/bCOIBIjmGMWSuHWaqUizS0SaY98r7L\nsBXf/4UsUEOt0qZFuh/0JViXRN+SggeIVGGl0phIUx5x3GV4imYAx1NlpUpbFumR36Y5v1mz\n4rqvXUukc36bC33yVXn4+tr8+mpRXzo5crNXXV2zsyC2N3Ra30qbEZ42qV/4cY+47jI8QH8A\nx1VluUpbFumoHvnNlPTZq1y342r3SIf81irX6uIt42SkevGgqtuq98PSqEVKLbqlQZFGPeK8\ny3DFxAwCR5X9mvPfft20SMU5rsZp7f50RHqqwtWiv/tsjnLVi/f8TmcHNfRI9fhFmu2WtEid\nyYaxw7BWF0dYF8xmBo5UZb+2KV/bdo+0vEj6Qo13Ul6Wf1KX6tc0i+dcobu6DIXZFEy4SOl0\nt9QXaVgj20uMZgvmMgNntZa/DpnT+YR1mCXSRGqGdp8LDe3y3aNyRynNhnlVi28Wj/ltNl/l\nTWyv+e3BLsUDdiqRLtXzqtK0vtg/ye8xmGSfa0S6HaYuuAw5czHRLf3UEWnII4a7DLseApoI\nmxWn+/npklGQJtLikw0ZWX+UNDMJRtdh3E89rfzI7ytzzy/c1eoUIlVPrjJFyp9XeqhPyM/f\nqh9sNVIynhWkho2p1BGp75HXXYZJ/nTDXM1psavpb76HUQxMf5/Mzm5GpOM7GwuWX9NKpOrJ\nVaZIWrPqSValSDf9wXd+ceZwyVjWzyNsWKWfWpuk6xH1LsNe/tTm/DPFnE5Ut2QsiBOJn55I\n9+Jyruq3T4tUXBBWP2G0EOlVv93+4LF8skH5VjFGfI+OUYOLNDLC+8nYJB2PnK92/V4dQ3X8\nuZE+R0CVDbNDkbKR3cMY282I1LxgiNR/uzU/0txDbfouLjJaRV8lQ6SWRw6dkTGAsyvY/ARB\njowqG2CHIh2zHuLU7Lj0Rao6kuM+ROp3S41Ihkd2Fg0M4EYKRtvNkVJlPXYgUn/2O9XX61eT\naX2RLuX099lOJOPJVMfyeaTlC4eZp+wIahUtlX6qJhtqj+YsmppBqArmNUHQDWNhyyItcIPI\ngeOxaXNEdkCkezllcJ8VyXwylX7tVj4hu5lseE2doiGqVZjd0k+5SNVh2HGLpmfg2vMDXhME\nNaKqzIRTpH8j/pwpzGVcpCRjaHmOoTOEjHOE+iKVt5c5pAMitYdxze1n6lGhvgdNZ/o7v4PN\nENJaRa1SLlKh0aBFI/4M9znS1nKZMNYe6d+GVHK81PwyeGZORlJ/aS/PMnjOanPW6oBIL33S\n6rmeqRsXqbn9TPWjnQOyl0Pm1ZhHAltFqZIWKfeobVG3A7KbIJC3lkuEMQ/t+iap+sso7XfP\nI4LwiOSL2A3JFZaP8H5S2qOqM2r8Ie3mSFxL/jDufaRep+QsUpoMneMGkdYLy1RSv2UWZer8\nd43nBIHQtWQO459s6KjkLtIIwyLV+1NfGcP/foy8jn/Nf3/JMP/9wx/+8M8SyhXLvx/T7885\nM8SfWyYtLJIF6JEK/jJJ+SG97fUe3rcVC7aJMPYeqT+2m/XRfLt71+Xme4g0H2ajyjjGlvpK\nv7GpJLvKuOCebOjNNjj2SPfWVQc340k1ECln1JNfbFwZQ3X+3OmScZkUvMpWCVt8+tt1aPdI\nkstdTxi/7heVGFPhUkTyaK79sCGshmC2YfN0HWrCmEwS2/bFijR4QNZ9H6k5JtuavpMh0i9/\n4UgxOhHCEGwYwoYcdMgI4xneiW37YkUahDDZ8L7qC+dO186JatXZDImxbAOnSH+xri6bbiVk\nq5jeda3e4jBJbNuPSyTXMxv44buvXWbAl/kdaQhmloyrYG5h4x1R85FqgcEksW0/MpHmiUWk\n/9oejfkHrt8qLBwqPlcv+Q/vxLZ9iOTGUj2SP6uG2TpUfNhY9jUp3ipzASI58Zdf+LLWaxVO\nDvXDPE0S2/Yhkhu89/5mnP1epVU4OzQQ5je8E9v2IZIb3DfR/wtnGB/9MPeOaDTMxySxbR8i\nubHHp1F4ONQP03iYFEmVeSJOpMdBHYqzhLguNd+ZSL4OFSG9V+gmya8yDqSJlN9ptbiKFSI5\nh3E4lDOQQt5Rkl1lXEgTKb/x7z2/jTZEcoGlI2rShl4kmiS2yjYtUtEYXskRIllTOMRasuGq\np5kkscr4w2SKlJl0gkgWGP3QGiUjDe+EVdlCYdJEOpenfD/VESJNM3AJERujYQSTBFXZgmHS\nRHpVreOTbci/QZGGdohWKpm7STKqbOkwaSLpe8oVC48jl0icF1IE35Cjkwprlcx5eBe8ylYJ\nEycSP5sRaXpiboXJhhJHk8S2fYjkxiZEmp/cXk8kR5PEtn2I5EbsIlkeIVpRJLfhndi2zxn2\nwRk2dolBsVXGm0Pn5fe5umcDT7EiFsmlFtYUyalTktr25YqU/jKokmpu2zC8fTqvnhREIpyp\nsHITszdJatsXLNKgSip1FEmpT9YyRScS7Y/I2k3M2iSpbV+0SCMqpS4iHZj3maISie8SIi9s\nwmx3lKS2fUEi/TLI77sqOYr0OlymnxjpSCwieY5mAzQxO5Oktn1BIg3S75IcRcpPatjXPhLH\n2q472VBgZZLUti9bpKH5Bkw2TISxrWgIkayGd1LbvmiRhqft2l+G3ze+XXiyoXr6ZfXsy/Ra\nFuCtn3hZDCuNxW4YI6yXEAURyaZTktr2OcM+eEs2PP3t3iMxFikd6JGK5zHXT2O+V635kPcO\nSWexG8ZDni+0VaROx8hnTZK7llJFmjwg67CPdBp/fDGFr+7tv59KPfQ17c/8u/o087s6PtPn\nQT8M2ljsh/nSDOaEtgrHsLnh3TbWchrmHmkExzMbFPs+UrdLuqnknajiFisndSl/0Tn35q4v\niDIW+2EedNZKaKtwDps2aStrOcU6Is2xukh696jcUUqzYV75i45K7xO99BvGYvbmVSXX/Gkz\nF5/qCncJ0fJhkyZtZi0nECkSNwMiZf1R0swkKNX9p7V41Qrcj/rrhVZdY38VhLYKQtjU8G47\naznOXkXKBnQn4/dPi3R8Z2PB8qtzdU32rEJbRUrZJOMmyV3LLYuUtboVhnZ33ceYv7PzT2vx\nlS+9i+8dLyGaXgWhrSIl/W0bNUnuWkIkJ/oiZSO7hzG2mxGpec9epACXEIUWadQkuWvJFvbB\nGUZn9aHdMRvYnarZhlqbYobh3Uw2vNWRIJLDnwCZrUJD2iQjO0py1xIiOdET6ZYfaa3mv2uR\nLuWc97m16CKSazcqs1V4hA2aJKFgC4eJFOl91RNkp9vIh93Ra2ialB+PTZsjspVIxVHYo955\nMhYtRSINRWW2Cp+wIZNEFGzZMIki3ZNyBynpn1ZAoytScYaQcY5QZUDxmw/tRQuRyLtzMluF\nV9jA8E5GwRYNEyjSS6mz7igep2KijIGOSPWh2Pqs1UqDlz5T9fxuL06L5DcnIrNVeIb1TJJS\nsAXDBIp0qU7Jrm9e7E23R/IOK2CYV5TZKjQ+K9Y1Se5ablmkRFUnrL6qkZcv/CJxzc3LbBUa\nr7XrDO/kruWWRTIaKONxJD6RGI9vSW0VGs9VbJkkdy25wj44wzxYQSQWk2RfQiRJpJZJctcS\nIjlBEan7q+uOSOSGFBhmDO9kFWyJMIg0RrtE5mBO5IYUGVabJK1g/GEyRTLgiXcWqf7FA4UQ\nuSFlhlUmiSsYexhEmijO5i8hWj7sG2NWhcgwbpF+N/K646Xm3BRraG2SmpyZE7kh+cN4Nkmx\noyR3LaWKlP5uUCXXm+hz4ybSTEcockPyh3FtEm2S3LUUK9KgSs430efGsUeaVknkhuQPY9sk\n3ySvJVPYB2dYzaBK/aXh9xfBVaR0yiWJG3KBML5N8u2b3LUUI9LvBvm7rkrxiZSOFkrihhQe\n9ifGLJFruUyPNNAlqd7CyPvLQBNpMoyJfYR9uT4GfTKMMUu0SEPzDSJEYjJJ4oYUHvbl9sjZ\nuTBGBIs0PG3X+Xfs/YWASGHDdBabSQLX8oMzrGB4+rsSZdQXiCQujHOT5AXjMklglfGLNHNA\ndvTnIJK4MHaRuIZ3AqtsgR5phPIy7dHzBSCSuDB+kZg6JYFVtp5Ic6wjEo9JAjfkEmFLiMRi\nkrwqq5oVRCKFsbCPsCaLwSR5awmRvMJY2EeYkeW/oyRvLSGSVxgL+whrZfmaJG8tIZJXGAv7\nCGtneZokbi3rVrUfkVhMErchlwlbZLKhwG94J67KIJJnGAdywxYUya9TEldlEMkzjAO5YYuK\n5GOSuCqDSJ5hHMgNW1Ykj+GdtCpr2hREooVxsI+wwSyqSdLWEiL5hnGwj7DhLKJJ0tZylyJx\nmCRtQ0YQNpJFG95JW8ttifRlyYftB8Ea/Cl0AfyxblEcnsyCHklc2MKTDSWEPklYlRktags9\n0jQQyZ11RCKYJKzKdioSg0nCNuRSYSuJ5L6jJKvKzPYEkahh/sgNW0sk505JVpVBJI4wf/YR\nNpPlZpKstdytSP4mydqQUYTNZTkN72StJURiCfNmH2HzWQ4miVrLVmuCSOQwb/YRZpFlb5Ko\ntdyxSN4midqQy4WtN9lQYD28E1VlEIkpzBe5YWuLZN0piaoyiMQU5ovcsPVFsjRJUpW129LO\nRPI1SdKGXDAsgEh2wztJVQaR2MI82UeYfZaFSZLWEiKxhXmyjzCHrHmTJK3lvkXyNEnShowk\nzCVr1iRBa9lpSBDJJ8yPfYQ5Zc3tKAlaS4jEGOaH3LAQkw0l0yYJqrK9i+RnkqANuWRYQJGm\nTRJUZRCJM8wLuWEhRZoc3smpsm4zgkheYV7IDQsq0lSnJKfKIJKXSXI2ZDRhlKxRk+SsJUSC\nSOuGkbLGhndi1rLXiHYoko9JYjZkPGHErGGTxKwlREoh0rph1KxBk6SsZb8JQSTPMA/khgWe\nbCgYGt5JqTKIlEM3ScqGXDhMhEhDnZKUKoNIORBpBiEi9U0SUmUDDQgi+YbRkRsmRaSeSUKq\nDCKVkE0SsiFjCvPL6uwoyVjLoeYDkbzDyOwjzDerZZKMtYRIFRBpvTDvLNMkGWsJkWqoJsnY\nkFGF+WcZwzsRaznYeCCSfxgVuWFiJhtKapNEVBlEaoBIk0gTqTZJQpUNt52dikQ1ScKGXCFM\nnEjV8E5ClUEkE4g0hTyRyk5JQpVBJBOItFYYW9Y3zrCUHDbScvYqEtEkARsytjC+rGx4J2At\nIVIbiLRSGGfWt/BrOdZuIBJHGI19hLEW7E+cYRDJibE1JJkktrluf7KhCHN9DPpkGOFnRlsN\nRGIJIyE3TK5Ibo+cnQtzByL1oZgkt+1vXCSlyjClvlXfdN4b+XYciOQERHJHtEjpt3AijbeZ\nHYtEMUlu2xcbxi9Sd3hnbU4HiOQERAobtoBInYuU1hNposXsWSSCSWKbq9ywBUTSw7s0vSTq\n+Ci/vajkYn60+Hi5dFXJNfu4Upe0/LC6vEklg0jDQKQVwpYRKf32j6pAf3vSCxfjo6ZIV/3m\n/Vh+pPw+IZUMIo3gbJLY5rr9yYaaQpCb+sdn+jwU3x7futupP9oWKXvzVn3V3yfZzx3VjVCy\nqeYCkdjCnJEbFoFIR/XMhnfl0O6VGntKXZGKN9/1949s6akOEMmRyTV0NUlu29+8SGVYpUk5\ne9e2pv5oax8p7X+f/+NassnGApH4wlyRGxaJSGlxSAkiFYQUydUkuW1fbNhCImVDu1QP0SZE\neg2LpH/uoY4QyRGIFDZsIZFu6phPGgyLlKjP9N17s5x8IE42TDeVvYvkaJLY5io3bCGR0mM5\n9/Dtm+FK9e5Fv3MaEUm/d3QvGUSaBCItHLaUSK0DsmlHJP3meWwf6aSSm3vJZhrK7kVyM0ls\nc936ZMNYmJ7GTl1OvTPehUhOQCR3IhBJZXtB6ftUnM9QnsWaf30tI9JcM4FITibJbft7E+la\n7CEl7+Lbb9WXbO/oPJUAkahAJHciECm9HZU6XN7Vt5VJZ3V4TSUQRZptJBDJySS5bV9s2FoF\n08M7t8vQIZITECls2HoFczXJoWTzTQQipS4miW2ucsNWLNg3iLQgECls2JoF++Z0jyH7klk0\nEIiksTZJbHPd32RDm2/fCon+C0dYD4iUQiQK8YmUU9nEEmZg0zwgUo6tSXLbPkRaLMyqcUCk\nHIjUAiKZQKQcuzW0NCn+VrF6mNiC2YbZNQ2IVACRlgoTWzCI5Ibc6tpHmNiCCf4TSwMibTpM\nbMH2KlKSYS4nUx82kLtLKTcMkw01QaahaNhttaT+YvxrA0RyByJVhDnCSEOISOsfdpMbBpEq\nti2Si0cQiQBEKgl0FiYNgkj1LlJ+leQXEx9cQWArcDWJZczpQOyRuCcb0tVPlt9HmNiCWYSF\nulKNBmEfqbs8BUQKGya2YLsTqRjHrSHSyhcU7yNMbMHmw4LdzYOGnKEdRCrBZEPOLkSynrlz\nWsNVb7okNwwiacLd8ZCG45kNibFsg9sarnk/TblhECkNejNrGjLOtSuBSBqIlEKkLo5ruOIz\nB/YRJrZgM2EhH/hDQ5ZIKz5Oah9hYgsGkdyASGHDxBZsOizoQ1FpCBNpvWdX7yNMbMEgkhvu\nazhRh5G2Clcw2eDqEUQaACJBJIjUg7CG47UYZ6twZvciOXsEkYaASIxZctdyPMzdI4g0yGhF\nxtgqAoeJLRhEcoO0hmM1GWOrCBwmtmDjYQSPINIwEElk1jphFI8g0ggjlRlfqwgeJrZgY2Ek\njyDSGMPVGV2roLHryQaINAxEcmfPItE8gkijDFZobK2CyI5FInoEkcYZqtLIWgWV/YpE9Qgi\njbNjkfY72QCRRqGv4UClxtUqRISJLdhQGNkjiDQBRJKWtXQY3SOINEW/XmNqFULCxBasH+bh\nEUSapFezEbUKH3Y62QCRpoBI7uxTJB+PINI03bqNp1V4sUuRvDyCSDN0ajeaVuHHHkXy8wgi\nzdGu31hahaAwsQWDSG74rmGrgmNpFYLCxBasHebpEUSaBSLJyVouzNcjiDSPWcdxtApRYWIL\nZoZ5ewSRLDBqOYpW4c/uJhsg0jwMa9hUcxStwp+9ieTvEUSyoq7oGFoFAzsTicEjiGRHVdUR\ntAoO9iUSh0cQyZIPzrB0T2FiCwaR3GBaww/OsHRHYWILVoWxeASRbIFIArKWCOPxCCJZ88EZ\nlu4nTGzBijAmjyCSPR+cYanosB1NNkAkW/jW8EN8q+BiPyJxeQSRXPgQ3irY2I1IbB5BJCc+\nRLcKPvYiEp9HEMkNxpqX3cRkZjGHcW5NiOQERAqatZeNSSMmkeT+ERMbJrZggsfpNKISSeyw\nWmyY1IJJnjmiEZdIUid6MNnghuhjGTQiE0nooQeI5ITso+s0YhNJ5sFwiOSC8PO9aEQnksjz\nHCGSA9LPQKYRn0gSz7wXGyawYOIvLqMBkTYdJq9g8i93phGhSAIvqhQbJq5gEdyAg0aMIsm7\nX4bYMGkFi+GWUDSiFEncHZww2bChMBpxiiTtnoIQyY4o7vZJI1KRhN0uGiJZEcf9p2nEKpKs\nBxhAJBsieSICjWhFSj8EPeRNbJikgsXysCsa8Yok6bGjYsMEFSyaxy/SiFkkOQ/CFhsmp2Dx\nPBCYRtQi+Zgkd0NuUqSIHlFPI26RPEySuyG3ONnQ305SSsZF5CLRTZK7ITco0sBWElIyNmIX\niWyS3A25PZGGtpGMkvERvUhUk+RuyM2JNLiFRJSMkfhFIpokd0NubbJhePtIKBknGxCJdmhW\n7obcmEgjG0dAyVjZgkikTknuhtyWSGObJnzJeNmGSAST5G7ITYk0umGCl4yZjYjkbpLcDbmh\nyYaJQbfc+qfhvdW+hPARugBsqNAFYEPINuHwZJat9EjOfZLcv4ib6ZEmt4jc+qexHZEcTZK7\nITci0sxcqtz6p7EhkdxMkrshtzHZMLcxxFYZkS2J5GSS3A25CZFmN4XYKiOyKZFcDs3K3ZBb\nEGl+O4itMiLbEsmhU5K7IeMXyebvmdgqI7I1kaxNkrsho59ssNoEcuufxuZEsjVJ7oaMXSS7\nDSC3/mlsT6ToN2TcItnupsqtfxobFMluW8rdkFGLtIF9VBpbFMlqc8rdkDFPNmxh1pTGNkWK\nevo1XpFcLgwTW2VENirS/DaVuyGjFWkjZ5bQ2KpIs5tV7oaMVaStnOtIY7sizXRKcjdkpJMN\nmzn7nsaGRZretnI3ZJQiOd83Q27909i0SFMmyd2QMYq0pUv9aWxbpIk/lKFLNk6EIm3q5jM0\nNi5SjHexiW6yYWO3Q6OxeZHiu69abCJt7QadNLYv0sgfTAklWz5shYJt7pbRNHYgUmw3cY9K\nJPIDSMVWGZFdiDS0uYWUbICYJhu2+HwqGvsQKaonxsUjks/zsOXWP429iBTRM0xjEcnvsfJy\n65/GbkTqbndBJesQiUheGkmufxr7Eamz6UWVbLGwxQrm1x2lgquMyJ5Eam19WSVbKmypgvlq\nJLjKiOxKJLMBSCvZMmHLFMy7O0oFVxmRnYnUmCSuZIuELVIwBo0EVxmRvYlU/zWVV7IK4ZMN\nHN1RKrn+aexOpOoPqsSSFYgWiUkjyfVPY4ciFY1BZMlyJIvEpZHk+qexR5FylYSWLJUsElt3\nlIptGWT2KRLjECVH7GqyFmwfVUZkryLt5M8rY9bHh9i1hEhhw/hUEruajKNhwWsJkUKHcakk\ndjV3Mj8DkYKH8ai08cmGD+lHDCCSgDBx57uIE6mqoAg2ZjggEodKWxbpY2dnVRGBSBpflbYr\n0v5OmCcCkQo2ermnZ9bHDi/hIgKRKjZ5AwKvrG6NiF1LiCQrjK6S2NX0yOrXhti1hEjSwqgq\niV1NatbHUE2IXUuIJC+MptLGJhtGKiG6jbkmEKkLRaVNiTRaARFuzPWASH3cVdqQSBMrH+XG\nXAuINISrSlsRaXDXiBo2jdwwGhBpGDeVxK6mU9bcOotdS4gkOWz6r7NjmAuBRJpfXbFrCZGE\nh1m7JHY1rbNs1lTsWkIk+WF2KoldTbssyz8YYtcSIsUQZtPKop5ssB7CbmBjLgdEsmDWpYhF\n2sKeIESKJ2y6vUUrktPk5GY25hJAJFumuqU4RXKZl5wNc0VuGA2I5MBoywteMuesD1eLpsIo\nyA2jAZHcGG5+EkrmkEWQaDyMiNwwGhDJlaFWKKNkVlmUrmg0zAO5YTQgEoFeWxRTspksukQD\nYX7IDaMBkWgseDeDhSYbPLqifpg/csNoQCQqZsMUL5K3RGYYC3LDaEAkHyqZZIvEYlEqsf4X\nCaMBkXzRMskVyX9AZ4RxBckOowGROPiQ2V4ZC6WRW/8QaUthXDLxlKwojfAqExlGAyKxhnHI\nxDINIv7Z7XLDaEAk9jDfcZ5HyT66vzuSKhMVRgMiLRPmIRNlk3z0FCqIqcqkhNGASMuFEWVy\n2yQjBpXEVmUSwmhYb7WkWcqw/Sm51bVS2MfHWGcxiuUmscqNscpCh9GwFalxJ6m/WCC3utYO\n+zCZ/OTsJnEwM+oqCxRGw1KkJIVIjGGTVn0Nf8xSQ7+CIYyK+9AOIjGHjeiC0+MChdHwEUlp\nvgCQDb81A6BH2nSY2IIJDqMBkcSFhX6sy97DaMxttXquGyKtFQaRwobRQI8kLgwihQ2jAZHE\nhUGksGE0HEXSX3FmQzxhYgsmOIwGzrXbdJjYggkOowGRNh0mtmCCw2hApE2HiS2Y4DAaEElc\nGCYbwobRgEjiwiBS2DAaEElcGEQKG0YDIokLg0hhw2hApE2HiS2Y4DAaEGnTYWILJjiMBkTa\ndJjYggkOowGRNh0mtmCCw2hAJHFhmGwIG0YDIokLg0hhw2hAJHFhEClsGA2IJC4MIoUNowGR\nNh0mtmCCw2hApE2HiS2Y4DAaEGnTYWILJjiMBkTadJjYggkOowGRxIVhsiFsGA2IJC4MIoUN\nowGRxIVBpLBhNCCSuDCIFDaMxsIiLRzvAUrmjNiCSSgZRBKH2JKJLZiEkkEkcYgtmdiCSSgZ\nRBKH2JKJLZiEkkEkcYgtq/LmRgAAA9FJREFUmdiCSSiZgCIAED8QCQAGIBIADEAkABiASAAw\nsKBIzWP9XJ7xtzxmaRJBReuWK2RZWkitMI2YRracSAntqbOL0yqNmFK1yyWqxqRWmEZOI1tM\npIT4+ObFkdouIJI7ghrZGkO70OvYptdghRCFSFLKVCOmke1cJEEj/jhEElRhOWIa2c5FMpZD\nI7VccgumEdPI9i1S2l0OiNj2KrXCcsQ0Mn6R6s5fzDpWFCWT2i4gEgUxjWzfPZKkkkktl9yC\nacQ0MogkpWBi26vUCssR08gWFymvelFTPVVppJVMarnkFiwV1Mhwrh0ADEAkABiASAAwAJEA\nYAAiAcAARAKAAYgEAAMQCQAGIBIADECk5VA5yeVVfDP0kUvr1fs5/+eoht5+nZU6PopXk2v2\nz1sd9HfnO2+pAQmItByq4p6OiHRpvfooTnG51S+ab7+KqMykq7p/qqt+t1AoeSxSeuAERFqO\nQoOsJ0newx/I+hiz/pOb/vqsX2y9fVYX7U7WCSXZiyqpOqRMPFEnv+0ViLQcjRDX4pvs/5M6\npa+DOmm1kuRhinQphDgk5YvttxNVRZb/X9Rn9dZl8VUBc0Ck5ag0eKpj1fxPWSfzeci+6L2h\nS2vA91a5D1f1Wb54GRgPGj1Sc67zRY30eGA9INJy1Bo0/cg5zTS5pJUrpinXfJfnmfVYzYtd\nke7qlquW/XtTt+bV62LrACyBSMsxINJLf3k3bxmmnPSbWT/zHhfplRz1P1c9a5d9sJy9S1+Z\nfCAwEGk5BkRKzS9py5R88ay7pTGRSo9yPtWlmr0bm1oHa4JNsBxV+37oHsNOpHrGvPd2Nuoz\nPEoP6l3uK3U/BoKATbAcVfs+6d0Zb5E+zQHcPdvRqhMhkgCwCZajOY6UWohU7CO1XzTefiij\nP9IdUlr3SNhHEgBEWo66e3mkFiJd1T3tvlgu6X+OZleVDxaL2bsUs3YigEjLUbT8w+VdfDMj\nUnkcKR0RqTXmO6lnWs7epTiOJAKIJIYLdVsonNkQHogkh+Q2/5kBcK6dBCCSHB40I3D2twQg\nkiDK65HcwPVIIoBIADAAkQBgACIBwABEAoABiAQAAxAJAAYgEgAMQCQAGIBIADAAkQBgACIB\nwABEAoABiAQAAxAJAAYgEgAMQCQAGIBIADAAkQBgACIBwABEAoABiAQAAxAJAAYgEgAMQCQA\nGIBIADAAkQBgACIBwABEAoABiAQAAxAJAAYgEgAMQCQAGIBIADAAkQBgACIBwABEAoABiAQA\nAxAJAAYgEgAMQCQAGIBIADAAkQBgACIBwMD/B3+KB3DHkBI9AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the transparency by contrib values\n",
    "fviz_pca_var(res.pca, alpha.var = \"contrib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 变量自定义作图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**由自定义连续变量着色**  \n",
    "在前面的部分中，我们展示了如何根据贡献和cos2对变量进行着色。请注意，可以通过任何自定义连续变量为变量着色。着色变量的长度应与PCA中的活动变量数相同（此处n = 10）。例如，键入以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dEhi2gIdgBgKyrVrfjmnTMWukXk4plL//HPbR1b35975+xZJdeLHD516MzcO2er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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random continuous variable of length 10\n",
    "# 生成随机数\n",
    "set.seed(123)\n",
    "my.cont.var <- rnorm(10)\n",
    "# Color variables by the continuous variable\n",
    "# col.var设置颜色\n",
    "# gradient.cols设置颜色渐变范围\n",
    "fviz_pca_var(res.pca, col.var = my.cont.var,\n",
    "             gradient.cols = c(\"blue\", \"yellow\", \"red\"),\n",
    "             legend.title = \"Cont.Var\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**按组确定颜色**  \n",
    "也可以通过定性/分类变量定义的组来更改变量的颜色，也可以factor在R术语中调用。由于我们的数据集中没有任何分组变量用于分类变量，我们将创建它。在下面的演示示例中，我们首先使用kmeans聚类算法将变量分为3组。接下来，我们使用kmeans算法返回的聚类来着色变量.请注意，要更改组的颜色，应使用参数调色板。要更改渐变颜色，应使用参数gradient.cols。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hm3NHuRdKseUSTLRy9AJDtEiqS0S0JE2jWJqUbSZj2WSPYPMIFIduR5ZPGIt8Cc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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a grouping variable using kmeans\n",
    "# Create 3 groups of variables (centers = 3)\n",
    "set.seed(123)\n",
    "# 进行聚类\n",
    "# center聚类数量\n",
    "res.km <- kmeans(var$coord, centers = 3, nstart = 25)\n",
    "# 将向量编码为因子\n",
    "grp <- as.factor(res.km$cluster)\n",
    "# Color variables by groups\n",
    "fviz_pca_var(res.pca, col.var = grp, \n",
    "             palette = c(\"#0073C2FF\", \"#EFC000FF\", \"#868686FF\"),\n",
    "             legend.title = \"Cluster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**维度说明**  \n",
    "对于维度描述可以通过dimdesc(函数[在FactoMineR中]用于标识具有给定主成分的最显着关联变量。在输出中，quant表示定量变量的结果。请注意，变量按相关性的p值排序。它可以用如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$quanti</strong> = <table>\n",
       "<caption>A matrix: 8 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>correlation</th><th scope=col>p.value</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Long.jump</th><td> 0.7941806</td><td>6.059893e-06</td></tr>\n",
       "\t<tr><th scope=row>Discus</th><td> 0.7432090</td><td>4.842563e-05</td></tr>\n",
       "\t<tr><th scope=row>Shot.put</th><td> 0.7339127</td><td>6.723102e-05</td></tr>\n",
       "\t<tr><th scope=row>High.jump</th><td> 0.6100840</td><td>1.993677e-03</td></tr>\n",
       "\t<tr><th scope=row>Javeline</th><td> 0.4282266</td><td>4.149192e-02</td></tr>\n",
       "\t<tr><th scope=row>X400m</th><td>-0.7016034</td><td>1.910387e-04</td></tr>\n",
       "\t<tr><th scope=row>X110m.hurdle</th><td>-0.7641252</td><td>2.195812e-05</td></tr>\n",
       "\t<tr><th scope=row>X100m</th><td>-0.8506257</td><td>2.727129e-07</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$quanti} = A matrix: 8 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & correlation & p.value\\\\\n",
       "\\hline\n",
       "\tLong.jump &  0.7941806 & 6.059893e-06\\\\\n",
       "\tDiscus &  0.7432090 & 4.842563e-05\\\\\n",
       "\tShot.put &  0.7339127 & 6.723102e-05\\\\\n",
       "\tHigh.jump &  0.6100840 & 1.993677e-03\\\\\n",
       "\tJaveline &  0.4282266 & 4.149192e-02\\\\\n",
       "\tX400m & -0.7016034 & 1.910387e-04\\\\\n",
       "\tX110m.hurdle & -0.7641252 & 2.195812e-05\\\\\n",
       "\tX100m & -0.8506257 & 2.727129e-07\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$quanti** = \n",
       "A matrix: 8 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | correlation | p.value |\n",
       "|---|---|---|\n",
       "| Long.jump |  0.7941806 | 6.059893e-06 |\n",
       "| Discus |  0.7432090 | 4.842563e-05 |\n",
       "| Shot.put |  0.7339127 | 6.723102e-05 |\n",
       "| High.jump |  0.6100840 | 1.993677e-03 |\n",
       "| Javeline |  0.4282266 | 4.149192e-02 |\n",
       "| X400m | -0.7016034 | 1.910387e-04 |\n",
       "| X110m.hurdle | -0.7641252 | 2.195812e-05 |\n",
       "| X100m | -0.8506257 | 2.727129e-07 |\n",
       "\n"
      ],
      "text/plain": [
       "$quanti\n",
       "             correlation      p.value\n",
       "Long.jump      0.7941806 6.059893e-06\n",
       "Discus         0.7432090 4.842563e-05\n",
       "Shot.put       0.7339127 6.723102e-05\n",
       "High.jump      0.6100840 1.993677e-03\n",
       "Javeline       0.4282266 4.149192e-02\n",
       "X400m         -0.7016034 1.910387e-04\n",
       "X110m.hurdle  -0.7641252 2.195812e-05\n",
       "X100m         -0.8506257 2.727129e-07\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#proba用于表征维度的显着性阈值，\n",
    "res.desc <- dimdesc(res.pca, axes = c(1,2), proba = 0.05)\n",
    "# Description of dimension 1 第一主成分\n",
    "res.desc$Dim.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$quanti</strong> = <table>\n",
       "<caption>A matrix: 3 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>correlation</th><th scope=col>p.value</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Pole.vault</th><td> 0.8074511</td><td>3.205016e-06</td></tr>\n",
       "\t<tr><th scope=row>X1500m</th><td> 0.7844802</td><td>9.384747e-06</td></tr>\n",
       "\t<tr><th scope=row>High.jump</th><td>-0.4652142</td><td>2.529390e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$quanti} = A matrix: 3 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & correlation & p.value\\\\\n",
       "\\hline\n",
       "\tPole.vault &  0.8074511 & 3.205016e-06\\\\\n",
       "\tX1500m &  0.7844802 & 9.384747e-06\\\\\n",
       "\tHigh.jump & -0.4652142 & 2.529390e-02\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$quanti** = \n",
       "A matrix: 3 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | correlation | p.value |\n",
       "|---|---|---|\n",
       "| Pole.vault |  0.8074511 | 3.205016e-06 |\n",
       "| X1500m |  0.7844802 | 9.384747e-06 |\n",
       "| High.jump | -0.4652142 | 2.529390e-02 |\n",
       "\n"
      ],
      "text/plain": [
       "$quanti\n",
       "           correlation      p.value\n",
       "Pole.vault   0.8074511 3.205016e-06\n",
       "X1500m       0.7844802 9.384747e-06\n",
       "High.jump   -0.4652142 2.529390e-02\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第二主成分\n",
    "res.desc$Dim.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 观测值分析  \n",
    "观测值指的是样本集中单个样本数据，R语言中数据每一行代表一个样本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.1 观测值基础作图  \n",
    "可以使用函数get_pca_ind()[ factoextra package] 提取个体的结果。与此类似get_pca_var()，该函数get_pca_ind()提供了一个包含个体所有结果的矩阵列表（坐标，相关性，cos2和贡献率）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Principal Component Analysis Results for individuals\n",
       " ===================================================\n",
       "  Name       Description                       \n",
       "1 \"$coord\"   \"Coordinates for the individuals\" \n",
       "2 \"$cos2\"    \"Cos2 for the individuals\"        \n",
       "3 \"$contrib\" \"contributions of the individuals\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind <- get_pca_ind(res.pca)\n",
    "ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fviz_pca_ind()用于产生观测值的曲线图。要创建简单的图，请键入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TkzSVIg0ucozuPI1flSLz7+lZgSfpRy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\nIBKCeAhEQhAPgUgI4iH/H5SPxIhH5PVuAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_ind(res.pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与变量一样，也可以通过cos2值为观测值着色："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Dim.1</th><th scope=col>Dim.2</th><th scope=col>Dim.3</th><th scope=col>Dim.4</th><th scope=col>Dim.5</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SEBRLE</th><td>0.007530179</td><td>0.49747323</td><td>0.081325232</td><td>0.001386688</td><td>0.2689026575</td></tr>\n",
       "\t<tr><th scope=row>CLAY</th><td>0.048701249</td><td>0.45701660</td><td>0.143628117</td><td>0.125791741</td><td>0.0507850580</td></tr>\n",
       "\t<tr><th scope=row>BERNARD</th><td>0.197199804</td><td>0.28996555</td><td>0.004294015</td><td>0.411819183</td><td>0.0007567259</td></tr>\n",
       "\t<tr><th scope=row>YURKOV</th><td>0.096109800</td><td>0.02382571</td><td>0.778230322</td><td>0.061812637</td><td>0.0202279796</td></tr>\n",
       "\t<tr><th scope=row>ZSIVOCZKY</th><td>0.001574385</td><td>0.57641944</td><td>0.239754152</td><td>0.001388216</td><td>0.0054654972</td></tr>\n",
       "\t<tr><th scope=row>McMULLEN</th><td>0.002175437</td><td>0.15219499</td><td>0.110137872</td><td>0.266486530</td><td>0.3892621478</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Dim.1 & Dim.2 & Dim.3 & Dim.4 & Dim.5\\\\\n",
       "\\hline\n",
       "\tSEBRLE & 0.007530179 & 0.49747323 & 0.081325232 & 0.001386688 & 0.2689026575\\\\\n",
       "\tCLAY & 0.048701249 & 0.45701660 & 0.143628117 & 0.125791741 & 0.0507850580\\\\\n",
       "\tBERNARD & 0.197199804 & 0.28996555 & 0.004294015 & 0.411819183 & 0.0007567259\\\\\n",
       "\tYURKOV & 0.096109800 & 0.02382571 & 0.778230322 & 0.061812637 & 0.0202279796\\\\\n",
       "\tZSIVOCZKY & 0.001574385 & 0.57641944 & 0.239754152 & 0.001388216 & 0.0054654972\\\\\n",
       "\tMcMULLEN & 0.002175437 & 0.15219499 & 0.110137872 & 0.266486530 & 0.3892621478\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Dim.1 | Dim.2 | Dim.3 | Dim.4 | Dim.5 |\n",
       "|---|---|---|---|---|---|\n",
       "| SEBRLE | 0.007530179 | 0.49747323 | 0.081325232 | 0.001386688 | 0.2689026575 |\n",
       "| CLAY | 0.048701249 | 0.45701660 | 0.143628117 | 0.125791741 | 0.0507850580 |\n",
       "| BERNARD | 0.197199804 | 0.28996555 | 0.004294015 | 0.411819183 | 0.0007567259 |\n",
       "| YURKOV | 0.096109800 | 0.02382571 | 0.778230322 | 0.061812637 | 0.0202279796 |\n",
       "| ZSIVOCZKY | 0.001574385 | 0.57641944 | 0.239754152 | 0.001388216 | 0.0054654972 |\n",
       "| McMULLEN | 0.002175437 | 0.15219499 | 0.110137872 | 0.266486530 | 0.3892621478 |\n",
       "\n"
      ],
      "text/plain": [
       "          Dim.1       Dim.2      Dim.3       Dim.4       Dim.5       \n",
       "SEBRLE    0.007530179 0.49747323 0.081325232 0.001386688 0.2689026575\n",
       "CLAY      0.048701249 0.45701660 0.143628117 0.125791741 0.0507850580\n",
       "BERNARD   0.197199804 0.28996555 0.004294015 0.411819183 0.0007567259\n",
       "YURKOV    0.096109800 0.02382571 0.778230322 0.061812637 0.0202279796\n",
       "ZSIVOCZKY 0.001574385 0.57641944 0.239754152 0.001388216 0.0054654972\n",
       "McMULLEN  0.002175437 0.15219499 0.110137872 0.266486530 0.3892621478"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j1tVeb6oxJzVIExszea61e6j20x199ZX+x5/Z58OwnKVAcALhHsgLAWFZ+VNGON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UcOH477vw3hBvytZnSlv9aldThc/XXajF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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quality of individuals\n",
    "head(ind$cos2)\n",
    "# repel=TRUE能够避免部分重合的点重叠\n",
    "fviz_pca_ind(res.pca, col.ind = \"cos2\", \n",
    "             gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n",
    "             repel = TRUE # Avoid text overlapping (slow if many points)\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你还可以通过pointsize和gradient.cols改变点的大小和颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Wk+4V7w9kzRL8hEex8yW1kO8AXvic2sh3Ql9ir7xcRZ7vdWKjFpyYU9bg8ANdz\ncMvZv3ynY01s1x/u7jm4xWNr5/b8oWNNbOJflp/b8wdjecea2I41sb2O+q4/3N39+r96PNeX\n9hFdTB7s/JqNY+oOADAIhl19v4icfXFlT+2LvY76vqp1v/6vZ5+5Va2u6K198ewzt7rns+7t\n/64m+bS6V7tfuPfsX77jUqHng/W9tS9qmdMDax9RZ2g9YwdgIPydhAvFw3bsYwcTiBmdG/+d\nvefeeeLsH+5WJcNu+UVs1udixl+v1+k5uOXcW48Nu/HHwz73sBaf6uxynHt77bm3Hou9/J6Y\n0bl6td76rfEPHYxJndB6cFfCoRfOvfVYz7QvG1fXapnTPS6D9bF9RB0zz9gFNv2m5u30X72/\nf9R5ZJ/Hms6Du3tf/e35msX39L76W/eavj/b53Ldi749vp+rNzX0bnuud92D5/u57sHebc85\nz7QO5OoDuYqz5p1+r3K+TZ/Hzfc2ASC6xKRnD//8r0b8y7H4b/4t7s5f99Zv7fqvG4wTZr2H\n3hARlbpERItPVQ/h9RzYbGwn7pZfxKROEJGzI8fHzlohIj17/2isEDtpvscO+Ng+oo5m1vVl\nwf3//pj1+zRLlrGk99XfOv/TdcZbRLTv/irmtm+5dMPjwluXQ146HPOrj7RxUz9z9W3POUu+\n5qHq7Dtjvvpv2iUzAru6S02/riKz74z5l//SElO8XEX8HDfV5okzZzMyMry0OUQ0NzcbxyEs\nAnhsLugzdpEwDhGCoVBMMw49B7ecfebWuDt/PWzWCrmw55xHagbOZVM6NQ7GQvdd69yPemkf\nUYpbsZ7puUGFG+erv9G++qh+9Hw6mX1nzFf/TUZPUmnGeXC38+Vy539+p1fEmFECu676n+j5\nq7/xe+PVnTtfcpZ8TWbfGZP3oFw66/zVW+zOd188f3W3gBUAv6+y8yXnW3/UvH5xv8dt50vO\nt/4os/IG+F0AIHL0tUuwun/a/dL9KtgBgTFnsBv4dF3P3SNUxoq5aUlPydecz/1ULkQrZ1PD\n+XTywDotLVM/Rbtkhpb/VK+I8z+/47zqVpcZvsB4uHqLvbf4Hg9XT8vUbvuWurrs2Saz7xzI\ndQO4ivbNnzr/8zvO6Te5TC5+2qb/46baTHzsCjHFP8cRFOxjh2g37JZfnHvt+z0Ht7i8Z8LZ\n5VAfPM6l9TWL1uuoV7diRaT3RK2IDLvxx750I/bq+3ve//WIR06yH7LJmPkZu6txu8AAACAA\nSURBVGAyJBjnP7aLSMziR4zpRKctuE+vEwrOj94QkZi8Bz1f/cYvawW/k4uzB/8q2m3fktl3\nOque7LNN/8dNtTn8r+WBfAeEgL/3VXkFBeAuNvsuETn7zK09B7foYa73RO25t9d6OUvtXdKx\nJrZ7+78by3s+WK/W1Q4/fbj7te+LSMykeT51Y9qXReTc22v1XVd6Dm5xbx9Rx5wzdkHUu+05\nEYnJe/DToo+3iYiMvsTzCar8421y05JgXV1b8kPXq0+43GN9LTFFC8Z1A7iKlpgSk/dgb9Ed\nzmvu0DzOF/o/bp+2eeMXPLcJANEmJj077kvPdr9w79lnbg3g9GEz7jP+qKVO6HryEhGJE+kV\nGXbjj3184WzsJQuG3fjjc289du6txz7tW/ZdLu0j6hDs+qTfz9UKfqfNmKeXO18uFxGP0056\nufPlcsl/aiAXvUhEn3bXbvu2j1cPlsCuos2Ypy35YW/xPe5rTfpts69x02bM673rn6SPNjH4\nxtzh9HEJBdN1QF+GTf9KzPg5vftf7dlnM74x1hed/z7WeFt22KwVEj+y+4V7nRNvG371N/x6\nHVnc/P+rZU7vrduqtrIzvuIM0Ytg54P6vzuP7Ovr0bGQ0pb8ULvt2/0GGvdnCkOxaNSXq2i3\nfdv53E9d1poM0Nmb7x3x4n8Et00MhC/ZLnSpjn3sYA4xqRNiZq3odwGsRx1rYo3vqBg2/SvD\npn+lubk58bOPI7s/ludeMmz6V2T6V+Tzv/Kv94hgPGPXp9iNnbEbO2N+9ZG0nuj9zpX6nmra\nHStFxGUvN50qV3UCvmjsxs7eJUUy+07tc19ySXXerx4sAV9Fs2TFFP7J+dxPnbvf8KtNL+PW\nkT6urzYRLt5zG3N1gO/8TXUDOQtDAcGuH9q4qdrXHxOR3ud/fr7oiptERD75wPMJqlzVkfOr\nLtzTzPmSvp8b65pzj+x8qfd717pu3uvp6nocdJ1CC/Tq/l3ls7TZd2p3rOytesr1un6Nm49t\nInzG3OF0D3AeCwEAg8acwW7gNyKNLZzfrW3nS+d/vGyOiPRWPeVssbuc5Wyx91Y9pdcREU0l\nlYa9rhf45INPj3rSmZIRs+ZlEen9rx8ay71c3V3AV/frKh5Oz3tIdr7kfPW3PrbpPm4+thki\ndpum/xqEyym93Y6OY5WOPfnHX9aOv6yd3lfUdcKPF3Krs0LXvb4Yk1zoIl24vh0QagOZeGPS\nDh6ZM9gFkfNM6/mlqd/8qSrRLFlawe9k50u9T+c7D+7+tObB3b1P58vOl7Tv/kq/f6rlLhCR\n3kfmGt+O5ax553yOyfW2dkmbMU+7Y6XsfMl5IVO6Xn33G3pIcp5pdR7c3fv7Rz/TQqBX9+sq\nHk4fN1X77q+cLpHUn3Hzsc3g8hjmBifh9XTZHbuXO6qXdTSUqpL2/cWndi489X5eb7cjpJeO\nOhaLJdxdAIDIZdrFE7EbOweyTbH7udoN9+ifY25a0iviLPlaryFyna/22VdjaZfM0Jb80Pnc\nT3sfmetas+B3+lu5+qJ9+RHny+W9xffEVNj11zzE3LTEOSKpt/ge96uLiHbHSu3Ljwz86r5f\nxXPPb/yy892X5LPn+j5uvrcZLP1GN7tNy7SGakaq8+iGriZbyvSy+My7Y+MzRaSny36m/qn2\n/cVdzZsSxi4N0XUBACZj2mAnA852582+U7v2Ts3tTRIxNy1xTr7KuWfb+aihqnl670LMVx91\nfu5Lzuot+myT9t1feazpTq1F6C2+x/nqb7UvfO/T8tl3xvxPvfOjN6T+787nfvppP6de4xLX\nBnR1n6/i4dzElJjFj7gHON/Hzfc2B87HCbnQZbvTNQUikpj16dqR2PjMpEkF7fuLHdXLCHYA\nAB9p5n45TwDBLkLeLm+a11oPXKiHwt/brKHIdo49+R0NpZb5jWq6zqPm5uZRqXFdzZs6jz7b\n1WRLyFqdNOmfhyWdf/+HegRtzB3OjmOVjupl8RbriIvvdUmEHccq1bkej4pI14ktncef72go\njbdYEyd9Lz7905v1qn3L/MbOoxtO1xS4tODL1b23LyK93Y5+v52IaJp25miFo3pZ2qyqEZlW\n3wbYnPhbQonqcRjgc3LG7Uuiehwin91u37hx46pVq0SksLBw+fLl2dkDfclTiJg82Ck+xrsI\niXQK/xPVDYVg191afeLtmfEWa0LWyriRubEJE9zrNDc3x9Z9q6vJZixM/9yuuJRcuRB9RuaU\nqMk/JTW3Qk9Xp/cVte8vNp6bNKVw5NQ1+o/eK6j24y1WYwf09j1ePSFrder0dT62LyKn3s/z\n/u1UsLPXlvceWOVy7tDE3xJKVI8DwS4q2O320aNHuxTW1NREZrYbEosnfElsEZXqMNTEpeRm\n3FQTM2Jcywd5TVsnHn9Zaz+49mzLDmOd3pZXuppsSVMKMxe2jLnDmZpbISJnLiy2OF/nXIs6\nmjarSkQ6jz6ryrtObGnfX5w0pdAyt27MHU7L3LqkKYXt+4v1hbd6BXV65sIWVaG7tdrY/rCU\nXFXhotmbje0rZ09uNbbf0VDqe/uddlu/305EOo5VkuoADLKNGzeKSEVFhdPpdDqdFRUVIvLL\nX/4y3P3ybEgEO7mwB5u/h2B6ASx3DdEK2WFJ2anT11nmN466fnvK9LKzJ7ee3DHn9L4ivYLT\n8ZqIJE54MCYuVUQSxi4dc4fTOCVmPKruUeoTYJ3HnxeRxPEr1VxgbMKExPEr9XIROXvydRFJ\nmlSgTo+JS02aVCAiZ0+85rF9dRfVZYJt5GUlAbff1fSXfr+dus+rWb5BqoNpuL8KYnDONTe7\n3b527VpN0/Ly8iorK10OlZeXa5qmaVp5ebnd/pntt2w2W15enqZp+fn5W7ZsMZaLyNKl52+A\nqA+lpa7/8owQQ+JWbDRiUl0X0qEILKWFbnmsUdeJLad2LkyZXqYWVRhvR7pzP2os8XiuewWP\n+moh1O27N6Xz/jDi0MHfEkq0j0PAd2Ndgl20j0OwOByO5cuXqyimlJSUPPzwwx4PWa3WDRs2\npKamikhlZeWyZcuMTW3evHnBAs/7gmmatnr16nXr1nk8Gl5DZcYOiFh97b6rZsVa96wa9B5F\nqItmb463WMcuks6jG8LdFyBoApt4Y7quL5s2bbLZbGVlZU6ns6amRkQKCgqMhwoLC9Ud1cLC\nQpvNtmnTJnVUpbrGxkb9xCeeeMLjJaqrq0Vk8eLFg/B1AkCwA8JsZE6JiLi/Z8Jla2LN8g0R\n6ekK5F0gCVmrRaSno14vOddeq5frH9TzbS6/fL+Ke/tJUwp9bP98D71+u/j0BUlT/o+InK4p\nMF4LiHb+pjRSnRdbt24VkSVLlohIdna2ynDGQw8++KD6UX1QhSJitVpFZOPGjdXV1erEqqoq\n9/btdntRUVFhYWFfk3lhR7DDkBbATdWg34eNz7SKyKmdC7tObNHD3Ln22vZDJSKilhGIiDby\ncyJypv4pVafjWOXxlzXHnnxfLjFizGIROXO4XOWhno76jqMbRCTe8nljhfZDJXq06jqxRa3h\n8P2LGNs//Y8CERk+ar6P7Q8fNdeXbzc87Xr1QX96DzAH37Maqc479eiburvq8VBm5vkHOdQH\n/VG5NWvWWK3WVatWzZw5Mz8/3+XxO8Vut69YsSI3N3fNmsh90tfMGxQDUWFYUnZqboWjetmp\nnQtdDiVNKdT3K4kZ9cW407b2/cXGTUMSL0y5eRefvkCtQjWemzSlUN8HzmOFeIt1xMXLff8i\nsSMmNG2daGxf36mu3/YTxi7tPPqs79/udE3BiDGLPe4LA0SphKKefp+3I9WFTm5ublVVVX19\n/eOPP15aWnrkyJGSkhLjhib19fUPPPBAhKc6YcYOiAQJY5da5talTC+Lt5xPWklTCi+avdll\n+WfqjA0p08v0Chk31aht3nwxcuqa1NwK1X68xZqaW+HSuKqg35xNmV6WcsV6v9YoJGatVPOL\ngbXv47dramo6v5kLk3YwnYSinr6im5dDMFq9erWIOBwe3rKtDulTceqDKtRNmDBh3bp1mzdv\nttlsOTk5evmOHTsmTpw4d+7cCE91wqrYiMX6Jt0gDIXva2MHZz2sR/yRUBgHHUOhMA4K46Co\nxa0VFRVLly6tr6+fOHGiiKioow4VFhaqcFZUVFRcXKxqikheXp7NZlPbDqsTrVaresyutrY2\nJydHPzHCMWMH+BrXwpjqAAC+WLBggdVqXbZsmaZpKtXpc3KLFi2yWq3FxcVqH7vi4mKr1bpo\n0SJ19N577xWRnJwc/cSVK8+/v3vDhg0iop+oG/xv5wuCHSDiQ2gj1QFA5MvMzHz66acLC88v\nyS8rK/vJT36iPqempq5fv76srEw/tH79en2ZxdKlSysqKtTaWDVXpz6LSHHxZ16HGOG4FRuh\nmFTXDf5QGO/MRk6e44+Eomn8rXUefyQUxkFhHKCwKhYRR+Uqj4nK5ZD3Z+O8V0ucWjhi/PJh\nyZ+ueFLVLrpxu/uljdf1vXsAAAwybsViiDqzr/jk6znn2mpdytv3/T+XnYEBAIgWzNgh6gW2\n9KHzSGXrB8s6D29Ivuwzq5zONtq6jj6XMHFlMLsIAMCgYMYOQ9SIcUtF5Mw+10diky8vOb17\nlftMHiJEU1NTuLsAAJGLYAd8xogJK4aPtnYc+GW4OwIAgN8IdhiiOo9Uikji1EKX8pi41MTJ\n3+uoK+1qtIWjXwAABI5n7BChjEtZPbyKuY+aur5Wtrrw+Czd8IwFiVMLHe/mpd9Sx9tIAQBR\nhBk7DFGJUwu95DYV+Drqyge3U+ifxWIJdxcAIHIxY4cIpU+5GXfd9HFyznuDZ/avPXtia/zY\nL3uZjYtNmJB6bZXj3bzhGfOHZyzwr+sAAIQJM3YYckaMX3620XbqzZnel77Gj7YmTFx95sAT\nLtvaDR9tFRH3ve5UiToKAEBYEOww5MTEZ6bN2SwibX8v8F4zYfI/n220ddavNxYOT58rIj1t\ne10qn3O8rx8FACAsCHYYioZnLEiYuPpso8370tdhydkjZ5S55L+4jFtE5NRbc7pP7dALu0/t\nOHPgCf0oQod97ADAC56xQ9Tr642x3p+9S5z6o466Use7eRl3tMTEpfZVLf7iJV2NtrOG/BeX\nmps4tfDMvuJTb81xqZwyqyIuNdefvgMAEEzM2GGIUssjRMTlTquLmLjUpKn/x6Uw+bI1F928\nK/nyEr1k5IyyUfNr1NssAAAIF83p9GlFIQaZcSnoEMdQKIyDwjjoGAqFcVAYByjM2AGIJuxj\nBwBeEOwAAABMgsUTAABgCNG0T5fcme+BtLDN2OXl5YXr0gAAYAjSNM2Y6jyWRLvwzNiR6gAE\nhn3sAATGS4DTtAGtJfVlCrCvOi69GvgMYhhm7PLy8qqqqgb/ugAAAMGlQqHOY3z0Xsd4aOD9\nCUOwI9UBAIDB1O/9VtPckI2sxRMuwzrE77k0NzeHuwuRgqFQGAeFcdAxFArjoETdOAydjfe8\n3J8N+uqNyAp25lucEjC2mtQxFArjoITi78EoxR8JhXFQGIcoFfQlupEV7AAAAEzM5V+nLgsp\nonLxBAAAwGDqNzCF5VZAKC5KsAMAABgMg/AwCcEOQDQZ4muqAATMS6IanOk691QXiqW4PGMH\nAACGBJWrwvJKsX7n6oI1mRe2YMdudgAAYPAFN8y5bDjscTGEquBera8NjQeCGTsAAIDA9ZUU\n9fLBvAvMM3YAoonFYgl3FwAgchHsAAAATIJgBwAAYBIEOwAAAJMg2AGIJuxjBwBesCoWQDjl\n/nmT+lD9hUXh7QkAmAAzdgDCRk91Lp8BAIEh2AEID/ckR7YDgAEi2AGIJuxjBwBeEOwAAABM\ngmAHIDzcV0uwfgIABohgByBsjEmOVAcAA8d2JwDCyd88xz52AOAFwQ4AAAwhmqYZf3Q6neHq\nSSgQ7AAAwJDgEumMhaaJdzxjBwAAzM9jqvPxaBRhxg4wOfddf+eOyZw7JnP+2MxR8fFh6ZKP\nVM9dHsKzWCym+Yc1gEHjS27TNM0Ef70Q7IAhZ+tx+9bj9q3HMx+96ooIz3YAMHC+z8YFlu2M\n7fd1el8P9vlyrl8IdsCQYJz3auvu/u9PDpbX7H+36cQd4y8OY68AINq5ZEEv0dC93Pdzfccz\ndsCQkxwX941LLxGRvxw+Fu6+AEBo+fvwXLQ/bMeMHTAUJcfFicjW43Zj4cuHj/7l8LGtx+1z\nx2R+fvxY98k8vcLKnCl3ZY27+7U35bNzgV5aUA/MvXzbvMd3/z07deQD07JV+btNJ149cuz5\nQw1zx2TeN2XStZZ0L91++fDR+7e+nfvnTcb227q7b3jptcWTsgpnXmGsXLzr4+cPNfztzlvU\nlwWAcBnMp/cIdsBQ1NbdLSJzx2TqJU/vrS2v2a8+q4fwPjndpscvlwrlNfv1z763ICJ/qmvY\netz++fFj+zplZc4Ul1P6bT85Lu7hKy5b+/E/vjNtqv7I4MmurucPNTx8xWWkOgCRIOjP0vWF\nYAcMOSe7up49UCciesB6t+lEec3+lTlT7pmYNTYx4diZjj/VNZTX7L82I11NoblX+E3t/ucP\nNeht9tuCcunIZH2GTz/lG5dekhwXpz/5d+vFY3JSU1z6rFdekJJ0+bhxLu1fd6GT+hzhu00n\nRORmQ3IFgDAK+rN0feEZO2BIyP3zJv3X/E1bymv2zx2TeeNoizr66pFjIqIymYiMTUy4Z2KW\nXi4i7zafcKlw36WXGNvvtwXFGPJUmyrVieHJv3eaTrj3X28/Mz7evf2c1JS5YzKNjwz+5fCx\nxZOyJiUnBTxiABAsg7mLCsEOGHLmjsn815lXPHrVFfptSjX3pjKZoj7rc3LqHqixgktm6rcF\nxbi7imrzhpde0xPnDS+9JiJrP/6He5/19i0Wi8f275syaetx+6G2dhE51Na+9bj9tnFjfR0R\nAOblb6iK9q3suBULDAku2/yaz+VpKSLyfvPJSclJ/2hx6CUAEHYsngAwqBZPynr+UMOxMx36\nlJua+lo8KUv9uDJnSnnNfmOFY2c6/Gqhr4v6uGpVb18vcWk/OS7uX2de8X93fTx/bOYP3qv+\n15lXsGwCgOJ0On3cxGQQ4leoQx63YgGIumv5p7rzyenYmY4XG46IyE0XFh9cm5HuUuFPdQ1+\ntdDXRf/7k4Mnu7pUybtNJ3L/vOl3nxz00kP1o8f2r84YJSLzN20Rkc9lZvg/DABMy5csFVje\nUqlR57JIwr2Oj+cGjBk7AHKtJV3NyRk3MVmZM0XfD8VjBb9a8PGUuWMy78ry8DIMvfKM//2L\n/vZbl/YnJSepib3Fk7KMT/sBgPQ3bzeQUOXLqyZ8fx3FABHsAIiIPDAt+9KRyV42KDZWMG5Q\n7HsLfV30veaTag3Ev868Yv7YzL5eX6sq//lA3faTLX21f9u4sc8fasibMN7v7w9gCFApqq/X\ntprD4D3NB780NzdnZHAvSYShuCACxyH3z5vc3/cQahE4DuHCUCiMg8I4QGHGDkD/1N3PDTfP\nmTEqTUTaurtfqDssItdkjApzzwAABgQ7AP178vqrH9rx/vI3txsLjVscDxqLxcJ9BgDoC8EO\nQP/mjsksv+Had5tPqIUOiydlXZMx6sbRFrYUAYCIQrAD8Bk/qio3/vh43kr14VpL+rWW9Aem\nZYejUwAAn7CPHYBPuaQ6jyUAgIhFsANwXl8ZLqKyXVNTU7i7AACRi2AHQKS/9BZR2Q4A0BeC\nHQAAgEkQ7AAAAEyCVbEAogn72AEYCPfXxZrsrxSCHQAAMD/3SGcsN02841YsABHDfnUBHAWA\nCNdXqvO9QrQg2AE4r6/0RqoDENV8DG3myHYEOwCfcs9wkZbq2McOgF/8imsmyHY8YwfgMyIt\nyQFAwAIIapqmRfXzdgQ7AACAwBnjo8dQ6GUprsuhgWdKgh0QKTq7z9bYGw42H3unbq+IzM++\nakr6xVMsF/t4uno5ROTMt0VafwAMNQHfV/Vr0s6lssdzXUqCHuaMCHZARGjr6nhh15t7G+v1\nktdrP3xdPpw2esKSWfNHxA0PY98iCvvYAYhqob7VS7ADIsKHh/ftbaz/Yu5Nl4+ZmByfICJt\nXR1vH9zzeu2HNfaG3HFTwt1BAECQhSLksSoWiAh/2fOOiFw78TKV6kQkOT7h5ikzRKTy/S3h\n7BkAIEjck5xmEJRLMGMHRITrJk57p25vW1eHHuxEZETccJdn1NRzeNWHP9nbWH/dxGk3TLnC\nkpzm0lT1kf2V72+ZNnpC7vhLc8dNqT/ZuO6tqvnZV9122TWqQlNby79vef6huV8am5quSv5c\n/dY7dXtVyTHHiU+aj6igqTeiqqnH5n5wy7Kqj/42JjX9tsuuUSXueLQOAHzR7/N5/iLYARHh\nuknT3qnb+8KuN2dPvGxsSnpaYrLHas998Lr+HN47dXv1NKZX2LZ/t8pkexvrVc2czCwReb32\nQz3YHXWcEJGGlib9RLVcY2xq+t7jdb9791W9Nb0R473gd+v/sbexPnf8pcH67n5hHzsAphGK\nh+0IdkBEGJua/i8LFv9t/8d6rvr89OsmXjR6wqjRep29x+v2NtbPz77q5ikzRsQNVzNz7xza\n+4XcG/U6Hd1n/23R10fEDVcRrfrwJ7njpszPvur12g+b2lrU9F714U9E5H+rt1078TIRaWpr\nEZEv5t4kIurq+Tfmqeu2nGn72WsVle9vMQa70SMv0ifkjDNzTW0tm/a8o3oYolECAN85nc7A\n7m+GaHHD4OyQR7ADIoUlOe0LuTfectnVJ9tbj58+9Y/jdX/Z847xFmpNY4OIfO6S6WqRbO64\nKe6LKvSj08ZMFBE133ZZZtbrtR82tzksyWlNbS17G+uXXr2g8v0txxwnxqamt3acEZGsNItc\nCGptXR3HHCdaOtoaWjxMj03J8LADi7rhKyJfzL1J5UUAgHehiHoEOyCyJMcnJMcnTBg1+tqJ\nl+1vOrp++0tpCckqKqkbpsaH8Dye7l6YOfIiEWloaZo2ZqK6D5s7bkrl+1vU3dijrc0iot+W\nffUf771e+6Ffl1BzhyLytWtvU4ESACJBAJN24dpQKVghj2AHhF9fe/mq3Yn1e6YBGxE3XN2N\nve2ya6oPf6Luun4x9ybV8l/2vPP56depmu/W/eP12g+vmzjtyosnJw6PHzki8bFXnvHeuB4E\nXZ72CxH2sQPgF7+yXQB/vbi079diiIBvFnvh33YnDofDZrOtXbtWX5q7du1am83mcDiC2y1g\nSFG5an/TUZfyzu6zxh+vmzhNRNq6OgK4xGWZWXLhKT1113XMyItEpPrIfhGZeNH5J/n+t3qb\niHwh98YplovHpqYPi4n10mZbV4dKddNGT/jx7fcNQqoDgAD4GNcC/kej08BLgx7b7+vcgPkR\n7CorK9PS0vLy8goKCvTCgoKCvLy8tLS0ysrKoHQIGIIuGz1BRNZvf2l/01E9zDW1tby5f7eI\nLL16gSq5JGOsiLx9cI+qU31k/4+qyv9c/ZYvl1B3Y9XaiIsSR+ol6haq+qxTyyk6u8+qDvTl\nhV1vvl774fzsq5bMmu/9BjEAhFe/sck0twJ8uhVbW1urwlxFRcW0adPGjh2bmZmpDtnt9mPH\nju3du3fZsmXPPvtsSUlJdnZ2CPsLmJElOU2tZli//SWXQ/Ozr9JXSOSOm1J9+JPXaz80PgN3\n3aRpvlxCvxs7P/sqtbpiRNxwtXmeXiIiqhv/vuV5l9P1FbVGamWGS38U9rEDEGlUdHO/9Wma\nSKf0H+yqq6uLioq+973vLViwwP1oZmZmZmZmbm7u0qVLt2zZUlBQsGbNmtzc3BB0FTCz3HFT\nJl40urbp8D+O16nAND/7qinpF6vH7HRLZs3fffSAumE6P/uqq8Zf6p63+qLWxk5J/7TBnNFZ\n79TtVXdp9W50nes2tn+up+fJrS8cPHHc9wuFFPvYARgIk8U4d/0vwSgvL1+50o9/fPtbHx41\nNzdnZGSEuxcRgaFQGAeFcdAxFArjoDAOUPp/xs7flEaqAwAACAv/VsXqbDZbXl6epml5eXlb\ntvCGcgAAgPALZDc8leqMJVVVVVarNXi9Oq+5uTnobQKIahaLhcfsAHPg3nEoBLJBcXl5+fbt\n26+//nr1o1pdEYpgN5R/y3laQsdQKIyDjnFQ+COhMA4K4wCl/1ux+fn51dXVxhKbzZaQ8Ome\nVR0dHTabLfhdAwAAgD/6D3alpaUzZ87Mz8/fsWOHKqmoqJg5c6b+8ok5c+ZUVVWFuJ8AAADo\nR//BrqWlpaKiorS0dM6cOWqpxKJFi/SH6qxWa4gesAMAdzxgBwBe9B/sUlNTly5dquKdiCxc\nuHD58uXt7e0bNmxwOp2kOgAAgAjh63YnKt5VVVVt3rxZRJYtW6beD+twOELZPQAAAPjKp2Bn\n3LVORKqqqrZv37569WoV79auXVtfXx/ifgIAAKAf/e9j52XXuurq6tLS0tLSUhkCL18bZCxc\n1zEUCuOgaFogu2+aEn8kFMZBYRyg9D9jp3atc16wa9eu8vJydSg3N3fdunU1NTUlJSUh7icA\nAAD60X+w63fXuuzs7Icffjj4XQMAAIA/+n/zhNq1zljCrnUAAAARqP8ZO7UYll3rAEQC9rED\nAC98eles1WolzAEAAEQ4X/exAwAAQIQLPNipF8UGsSsAAAAYCGbsAEQTi8US7i4AQOTq/xk7\n79Ny+lG2DAUAAAiv/mfsCgsLB6EfAAAAGKD+g92aNWs2b94sIlar1fgKCnXU5UcAAACEi0/P\n2C1YsKCurm7cuHFz5sxZu3atw+EIdbcAwCP2sQMAL3xdPDFhwoR169ZVVVUVFBQsX768uro6\npN0CAACAv/xbFWu1Wuvq6kTE5SVjAAAACDu/tzuZMGFCVVVVWVlZKHoDAACAgAW4j93KlStZ\nMwFg8LGPHQB4EYQNinkFBQAAQCTof4NiAEB0efLFn+ufH7rrkTD2BMAgmb9X7gAAIABJREFU\nC0Kw44YsAEQIY6QzlhDvgCGCd8UCiCbsY+eFe6rz5RAAMyHYAQAAmIRPwa66ujo/P1/TtLVr\n16qSysrKvLw8TdOKiorq6+tD2UMAQP/6nZNj0g4YCvp/xq62tlbfjrigoGDcuHFHjhwpKChQ\nJcXFxcXFxXV1dRMmTAhhNwEAANCf/mfsNmzYICI1NTVOp7OmpmbZsmUFBQWbN29W+9ht3rxZ\nRJ5//vmQ9xQA2McOALzqP9gVFxeLSHZ2tv5fEVmwYIHxgz6BBwAAgHBh8QQAmEG/G5qw4wkw\nFPQf7AoLC0WktrZW/6+IbNmyRX3YsWOHiJSUlISqgwAAAPBN/8Fu+fLlIpKTk6NpWk5OTkVF\nRUlJycKFC9WbxObMmSMiixcvDnlPAYB97LzyMifHdB0wRPS/KjY7O3vXrl2lpaWlpaUlJSVL\nly4VkXHjxi1btkxESkpKFi9ezJJYAIgEKsDxSjFgyNJ4IVhkam5uzsjICHcvIgJDoTAOCuOg\nYygUxkFhHKCweAIAAMAk+r8VCwCRw2KxROx9htc/evWjul333/5P8XHxemFXd9evX/kP9dnj\noSsnzpx/5W2D3VcAJsWMHQAEhyV1tIic6Wo3Fp5sO6F/bu1wGA+pmuosAJHMbreXl5erZaNF\nRUX6JiERqP9gp/lmEPoKAJEsNTFNRNo6TxsLj508IiLW2V8SkcaWY8ZDqqY6C0DEstvto0eP\nXrVqlfqxuLg4JycnYrNd/8GuoqJiEPoBANEuLekiEWly2I2Fb+19/ZLRUy4ZfamIbNn9ivGQ\nqqnOAhCxNm7cKCIVFRXqZaoqF/3yl78Md7886z/YLV26VH0H9brYvoS+qwAQ0fvYjUxIEZEj\nJ+v1klNtJ0VEpboFM27XSxRVU50lIk2t9g/273zyxZ8/+eLPbTv/VHtkr15TFZ7uaLXt/NP2\nmm2q5Nkdvz3T1a5OcamvNDTXvf7Rq+poQ3Od8ZB7g8b6T7748+0125pa7QJEIbvdvnbtWk3T\n8vLyKisrXQ7pd1TLy8vt9s/8IbfZbHl5eZqm5efn6y9iUOUiorZ70z+UlpaG/JsExKfFE0uX\nLj19+nRBQUFVVVWoOwQA0evKiTM/qtt1pqs9MT5JRJocjSIyOm2s/t+W9pMXJY8SkTNd7Qcb\n9185caY68WDjJ7adL+jtHGzcf7Bxv4hkj5umF35cX32wcX/OxZfrJZt3v6yqudffXrNt577t\nxtZmT50zJ+cmY2+NDbp0YOe+7Tv3bf/i9V/JypgYpLEBBoPD4VixYoWKYjabzWazHTly5OGH\nH3Y5JCKrVq2y2WwbNmxITU0VkcrKSrVBr4iovXs3b968YMECEfEYflavXj0438hfvi6eWLly\npYi4JF8AgJFaCdF+Yf3EkZMNIpKSkKr/95D9gDrU/tmVEypULb7hvofueuShux755sLVIvLy\nhzZj4+nJGQ/d9Ygx6mWkZN5/+z89dNcjX7z+KyJSc/TvqryhuW7nvu2zp85RR++//Z9mT52z\nc992l0k4Y4OqA99cuFp1YPEN94nIJ8dqgjk6QOht2rTJZrOVlZU5nc6amhoRKSgoMB4qLCxU\ndxoLCwttNtumTZvUUZXqGhsb9ROfeOIJj5eorq6WCH7nlh+rYquqqvR5SACAu/SRFhFp62gV\nkTNd7R/V7Zo9dY7a4iQ+Ln721DlqPk+vo6+cUHEqNTG1qdV+sPGTj+ur3Rsfn+H6jp/cSbNU\n42peTU3aicjhE/UiMmvytfqlZ02+VkQamur6avCS0VNEZN/Rmobmuq7urrEXXfzQXY+wDwui\nztatW0VkyZIlIpKdnW18WkwdevDBB9WP6oMqFBGr1SoiGzdurK6uVid6nKiz2+1FRUWFhYVq\nMi8CsY8dgGgSyfvYiUjyiGQROdV26pLR4jjjEJExaWP1o+PTJ+zct91xxpEYn3Sq7ZR8duWE\n8eapR+r2rvcSRbWj75+ne2vv67OmzPZ4+vU5Nx1s3P/W3tdF5JLRU2Zecg03YRGN1KNv6u6q\nx0OZmZnqR/WhtLR03bp1IrJmzRoRUUtfV69e/ZOf/ESvqbPb7StWrMjNzVWVI1P/M3bl5eV+\ntehvfQAwDbUSQsUjtdFJRsqn/9+QPjJDL1d19JUTH9dX79y3/cqJM794/VeW3fyNFbd+d5B7\nbknJfOiuR5bd/I0bp80/2Lj/f3f8wbbzT6yfwNCRm5tbVVVVV1e3evXq0tLSFStWuGxoUl9f\nH/mpTnyZsbv22mvz8vK+973v9TvruGXLlieeeCLCvzAAhJR6mu10R6va6ESPbiKSGJ90yegp\nb+19ferFOaqmfkjthKLf+uzq7hpIH9QaDpcXXfjCkpJpScmcenFOS/up/93xh4ON+x+665GB\n9AQYZCqWORwO90k7dchut6upOLUk1mUNxIQJE9atW7d48eKFCxfabDb9/sCOHTvmzJlTUlKi\n1mFEsv5n7HJzc0tKSp544gm1bLi6utq4PNhut1dXV1dWVmqa9sQTT5SUlOTm5oaywwAQ0dKT\nM0Rk39EaETGuYFVUiTqqahqpzVC6urs+OPDuQPpw6dgcEfngwLv6azAamuuefPHnH+zf2dcp\naqOTY6eOisjIhBR210OUmjt3roioJRH19fXGdyioQ0899ZT6UX1QhSKiNjpRs3SXXnqpXHjq\nTkRqa2vnzJlTWFgY+alOfHzGLjs7u6qqyrgS2F1FRQVLKwCEWiTvY6eMTEyVC3daLxqZ7nJU\nLYM9fx828dMZhTuusr78oW3DG+td6p9qO789il+yMiaqiUPjQ3uXjJ5y2XjXoKm7bPwVH9Xt\nev5vzxgL1d57QBRZsGCB1WpdtmyZnlj0OblFixZZrdbi4uLi4mJVYrVaFy1apD7fe++9Npst\nJydHb0rtByIiGzZsEBHjiUpkPu/rx+KJpUuXLlq06M0336ytrdUXD5eUlGRnZ998880eH1QE\ngKFGrZ9QLCmuD18bU5qxZva4aWd7zqobsrOnzrls3PRzvecq3vzvIycbAgh2IjIn56b05Iwj\nJxs+qtslIgtm3D559KV9LbYQkbEXXbzs5m98cqxGZcHZU+eMSRurtlYGokhmZubTTz+dm5ur\nQlhZWdndd9+tDqWmpq5fv37jxo1qhYQ6pKcXNTn17LPP2mw2q9W6cuVKfcbOJc9FOC0y8yaa\nm5szMlxv0wxNDIXCOCiMg46hUBgHhXGA4sc+dgAAAIhkBDsA0cRisYS7CwAQuQh2AAAAJkGw\nAwAAMAmCHQAAgEkQ7ABEk8jfxw4AwsinYFdbW5ufn69pWn5+/pYtW1yOGrd1BgAAQLj0H+xq\na2tzcnJKS0tFpLS0dOHChUVFRaHvGAAAAPzTf7BTb9KoqKhwOp1Op3PXrl3V1dVkOwAAgEjT\n/5sn1G1WYzW73b5ixYrc3Nw1a9Z4rICBYw9xHUOhMA6KpvG+nPP4I6EwDgrjAMWPd8XqMjMz\n169fv2LFirVr1z788MNB7xMAAEAAeu4eEYpmYzd2hqLZUOg/2JWVla1ataqyslK9H1dRL9md\nOHFiS0tLKLsHAAAAX/X/jN3dd98tIsuWLXNZ+jphwoTt27cXFxeHqmsAAADwR//BLjMzs7Gx\nsayszGq1uhy6/vrra2pqCgsLQ9M3AHDFPnYA4AWPIUcoHoPVMRQK46AwDjqGQmEcFMZB4Rm7\nQBZPDFBeXp7+uaqqavA7AAAAzCk23B0It8EOdnl5ecYw5/IjAABA4Ib8q1L9GACHw1FeXq71\nIXRdBACdxWIJdxcARLCY0PyKHn7M2K1fv76goCB0XQEAABgIpzbUVw74EexUqtu+ffv1118f\not54mfkzLoUz/pPdxOXNzc0R1Z8wlquhiJz+hKuccWAcXMqbm5sjqj/hKmcc1IeoG4eQLN/k\nGTvfq65evbq0tDR0qU58/j3uq5qZyo3rmyKhP2Es14ciQvoTrnLGQcc4KOqPROT0J1zljINi\njnEIgqi6bRoKfgS7n/zkJyJSWVm5aNGi1NTUkHUJAPrEPnYAvCHY+V41MzPz61//+pw5czwe\nZT88AAAQZkM+2PkxAGvXru0r1QEAAISfFppf0cOPYKcvnnB6ErIeAgAA+MQZG5JfUcSPW7El\nJSUFBQUDXDxRVVXFmycABMxisfAvSQB9YrsT36s+/PDDIlJeXn733XdnZmYGfEnCHAAACIkh\n/4ydH8FO32Ru1apV7kf5NzQAAAgvJ8Eu3B0AAAAIEoKd71WZkwMQduxjB8Abgl24OwAAABAk\nUbU1SSj0H+zUo3VOp9PLi1yF+TwAABB2MUM9jTBjBwAAzIIZu35r6FNxzMkBCDv2sQPgBati\nmbEDAABmMeRvxfqabB0Oh81mKyoq0i5Yu3btli1bQto5AAAAPwz5d8X6NGNXXV1dVFRks9mM\nherVsVartaSkJDs7OyS9AwAA8N2QvxXb/wDY7faZM2fabLaysrK6ujrnBTU1NSUlJTabLScn\nx+FwDEJfAYB97AB4ExOaX9Hj/7d37/FR1Xfi/98zk8mFhIRLJoRbuEkIIAZQEayIXKxKTdQV\nK1gvuxUo/my7Vmm7Vfi6rmjXXVC3u92mQdy2asHWbpUg1C2CCJWLconILVwTbrkMIYHc5/b7\n44PH45nJZHKZzO31fOTBY+ZzPvM5n3MM45v353La7qwaby0sLFywYEFWVpZWnp2d/dRTTxUW\nForIhg0bgtdFAACAQHjMQfmJIG139ve//72I3HXXXT6PqvItW7Z0bbcAAADaLURz7Ew6bVYw\nVGutvGPaDuzU1LqMjAyfR1V5QUFBJ/sBAADQWWZPUH78MplMHh2fwZnn6/wc7eQNYLsTAJGE\nfewA+BMJK1hVIBikxgnsAABAlIis+XAShCCPwA4AAESJpP/n0r9tXGZpdwtLXIaSDjTih3ck\npx+67b6h2M7P5gMAAAiqxhe+HoS1P3gxttChRtpFH8x1PoFHxg5AJGEfOwB+eCItDdXlk+3a\nDuyYpwwAACJDeM+xC+qyCYWMHQAAiBaRlrFj8QQAAEArwjtj51+XBHkEdgAiCfvYAfAjJHPs\nDJsSt2sxRGsbGncYgR0AAIgWIRqKbS16M5T7rMZQLAAAgA8Rt0FxlyOwAwAA0SLSFk90OQI7\nAJGEfewA+ENgF+oOAAAAdA2POdYXVxHYAQCAaMEcu1B3AAAAoGtE3CPFuhyBHYBIwj52APwh\nsAt1BwAAALoIgV2oOwAAANBFmGMX6g4AAAB0DebYEdgBiCTsYwfAHzJ2oe4A4EPtqq/+alpF\nakUSxj9jveohS1q2z/r1f73LWVYUl5WXfOt7/lvT+Gww8JoBnlpEPC21jtMbXOUftxwuUK3F\n9Z8eN2CGz8oAgE6J+YxdzEe2iBDN+16oeyfHXVfmfch1odhZViQizrIi14XidjXoqi3pTM02\nT+1urGzY8nDjRw+oqE61Vr9hVv1f7/K01AbYVQBAgDymoPxEEAI7hK+0R91pj7odd1WmPepO\nuuX3ItJyZKV3NZf9UxFRFdRrP61pP6q+49gbnanZ5qkdx950lhUl3VTY84Fy1VrPB8oTxj/j\nLCtynN4Q0F0AAATOHJyfyBFRnUUMix8xV0Sa971gKPe01DZuWxiXlRc/Ym5cVl7jtoUBZsJa\na7BrT920a7GIxI+ab07KUCXmpIyEcYtFpPGjBwLpJwxsNluouwAgjJmC8xM5COwQSeKy8gwl\nLvtuEYkftUD7U5V0g0BOHZ+zSETcjZX6QlN8msredU8/ASB2eMxB+YkgEdVZxLCW42tEJGHs\nPxrKHSffERGLbZL2pyoJtMHxz3S4ZiCnjs/5nog0blvgKFvnc4IgAKArxXzGjlWxCF9qjapa\nFSsiSbf83rCY1F1X1nK4IGH8M2qg05yUkTD+meZ9LyTk/pM5JctnawYq09aBmgGe2tI3N2XO\n4ZYvXm34a74qSZy03NLvxriMyW1dPQCg/SIqCAsGMnaIGO6aA4alqc6KT0QkbvC3tBL1WpX7\nlzD+mZ73n/KO/wKsGfipLWnZSd/4754PlCfnfZJ0U6GzfEt90Y1Nu5e2eV74xD52APzwmD3B\n+An1ZbUDGTuELzULzW63p6enu2pLWr54te6dnOS8T7R0l1p/UF90o+GDjR89oFY8eLcmIs37\nX3aWb7EOndNaVBdIzXadWkTMSRnmpAzJmBw/ar7z3Kb6DbPMKUPiR833d/0AgPYiYxfqDgAB\nsaRlJ17/cxFpLv65KvG/ZZ2fo9arHnSWFdW9O6HNTexaqxngqWtXmX2O6qoB5cZtC/2fHQDQ\nXuxjR8YOEcMUnyYiakNgEXGcekdEvAdJ3XVll98e6jj1jqVvrs92zEkZyXdsrN8wq2nXj1t7\nXIT/mgGeOnHS8qZdi53nNhmmBrI1MQAES8wnrGL+BiBCeFpq1erUxEnLRcTdWNm874X4nEXe\ng6TmlKz4nEXN+14wbDKiFzdgRnzOImdZkaNsnf/z6muqDFztKrPa067NU8dl3Ski9RtmOc9t\n0oI5V21J8/7l8uW2xmgv9rED4AcZOzJ2CF+GVbGKddgcEXFV7ZIvNxPxFp/zvZbDBa6qXeas\nO1trPCH3n1oOFzT8NT/1oYsqF9hmTZ/dM2xHpz+1JS076ZbfN370QP2GWcY2xz/jcyoeAKBT\nIioICwYydogMcVl5STcVaqOfLUdWxmXltTbYaumbG5eV5/P5YxpzSlaPW9eKSMuRVX6q1a4y\nX357qP8K+ol0hlPHj5jb8/5TSTcValsrJ4x/JvmOjYnXPu+nTQBAB8X8I8VMHk8kLeKNHWop\naKh7ERZCeCt8Ln1oTbCfJMGvhGIy8a11Bb8SCvdB4T4oVe8FJWVnuytivnYYigUQSdjHDoA/\nDMWGugNAmGpXuq4D9QEAXY7FE2TsAABAtDDF+r+xCewAHzqWfqtdZQ72TDsAgB8eU6wHNrF+\n/QAii81mY/EEgNZ4TJaQnNdk+mq8trXvKH0dfbVAPhs4AjsAABAl3Kb47j+pYbW+n8X73uWB\nfzZABHZAbDG9u1G98Nxt3DYZACKdJ+YDm1i/fiCmaFGdek1sByDKhO0cu27bgzNMrx8IrbRH\n3R1YP8HKiW7APnYA/Bh0e7n+7em/DGxvC4NvP2so6UAjPnXtXLrWENgBAIAoUfbBkK+9b38C\nz9hChxrxqWvn0rUm1rd7AVrT3vRbRKTr9GOvjMMCiD5usQbjp/Md67bl/GTsgNhCPAcgirlj\nPrAhYwe0KvAkXESk66KDzWYLdRcAhC+PWILx0/mOGTaxC55YD2wB/1TE5mchBSEdAIQPtycC\nApugrpCNgOsHQs5neEdIBwDhpkvmw7WXx+NpbcWrFsMZ6gTy2Y4hsAMCRSQHAGEuVBsUB/Ko\nicAfR9EZ3bRdXsfY7fZQdwEIF2+UX3jy2Onnhg34/wZmGA7999nKZ0+ee/mqwQ9l9rVt2yci\nVTeN927BcEi9NXhycL9vZ/QekZTo54MiUtXi+NGx0x9UXyocNeQeW2995QP1jXsuNzx57LSI\n3NYn9bY+aTemJWsNVrU4xuw68PeZff/9qsGtXemPj53+TfmFvdePGZQQgkcDAeg26enpXd7m\nwb98o8vbFJExt/8tGM0GQ1hn7ILxnzxS2O32WL58PW6FcpfTtbmu8dmT5+aOGJKdkqyVl9TV\nP3vyXF5m+ndHjUizXvkb7eeO+b+ZL5+uePl0xZFZU/Sn8P7gfxw6/kH1pdXXXT13UKa+zspT\nZxfuO6K9/aD60gfVl0SkcPzoBUMHiki6yJJRDcuOnPzZ1dnep1CX85vyC4uGDhw/cIDPHvL7\noOFWKNwHhfuguLtioUNEC+vADoAmNc7ydPawonL7K8fKfjV+tFa++IujIvJ09jAtqmsXw+4n\na86Uz/vsizdOlz8/ekRrH1l66PiyIyeXjBrmK6o7lJeZ/vzoEUN7JKn+FNdeLjh5ZuG+QyKi\nYrs5AzKWHTm5xV7jM7DbU3NZRB7J8h3VAYB/npjf7iPWrx+IIJP7pC0ZNazg1Nmi8iuP1Soq\nryoqty8ZNWxyn7QuOYWK1ZYdOdlahTVnylVUZ4j8yhqaVFT32oQxuWk9tSgzN63nr8aPXjR0\n4MJ9h8oamlRJXmb6wn2Hah1O7/bnffZFXmZ6V10OgFjjkrhg/IT6stqBwA6IJAuGDBSR/B3F\ntQ5nrcOZv6NYK+wGReVVKvDyzud9Ul0jIk9nD8vwNTFOZeBUHRF5YFCmiBy6XG+otqO6Vjva\nGvaxA+CHx2MOxk+oL6sdIqmvALJ6JK6dnCsiH1+4uLvmkoisnZyb1SOxrc8Fas2ZchFZMmqY\n96Hi2sv5O4pVTs776Bb7RREZnpzks1lVruqIyAxbHxH5bdk5QzVVoo4CQAe4xRKMn1BfVjtE\nUnYRgIjkZdryMtNXnjorInmZ6XmZncpgmd7d6F3oMwU4fvNOP+0UnDorIj7TdVp5wamzanZg\nRkL88qtHLv7i6M+yh2lRaVlDU8Gps0tG+c75AUAg3DGfsSKwAyLP8qtHjtq4XUSOzJrStS0v\nGTVswZCBraUA8zLTi8rtzx46rl+90TGzbH1E5JPqmqweVwZe1UDtnAHGzVwAIHA8KzbWrx+I\nRNp6Up8LS9tFWxW74ljpFvvFOQMyWovq9k2/oX9iwvy9BwtOnZ3YK1UtcdUsGjqw4NTZWofT\n5+JctU5ike4jagnFvM++0JbWqtl7uWk9/Xe4qqoq4IsDEHPI2MX69QNRJi8zXb4MpPRUiTrq\n00OD+xeV28dv3llSZ1zToOSm9VRDqCKycN+hTVXV+qPT0nuLiJr2502VqzoaFRqqBRPqT0Ow\nCADt5RFLMH5CfVntQGAHRBUVPHkvOPUZWullJMR/+I2J8uXGeK3JTknefvP1IjLzb3vU9iXK\njX16icirx8sqm1sMH6lsbnn1eJlWR3Nz394i8n6FXftTlQBAh7nFHIyfUF9WO0RSXwG0Sc1d\nm/LxpyoHpuyorlWh1Sy/C05n2PosGjqwqNyu7ZPn0+Q+aauvu1pEvv/5YS01mNUjcfV1VxeV\n2+fvPVhce1mrXFx7ef7eg0Xl9sLxow2DvGnWuOVXj1x25GRx7eVlR04uv3pkx/ZYBgCNR8zB\n+An1ZbUDX6NAVMlN67lk1LBlR05O+fhTw6HV113d5gy2n2UPKzh1Nn9Hcc23bvETZs0dlHng\ncv2yIyeXHyvV9rRTs+XmffZFUbnxKc/aI8UMVKCp1tv6Dzo1NpstnJ9wDSC0XJ5IGjYNBgI7\nINo8P3rEnAEZG6uqtUHVwvGjp6X3CmSlhdonL39H8WulZ5+6aoj/s4jIsiMnx/ZM1hZAzB2U\nObFXzy32GvVIDLUbi59TqyUUqmabQScAtCmy9pwLBhP/9g1PPM5Zw61QuA+KycS31hX8Sijc\nB4X7oHy4/u+D0ezM2b8JRrPBQMYOAABEicha6BAMBHYAIgn72AHwwxXzQ7EEdgAAIEqQsSOw\nAwAAUSKytiYJBgI7AAAQJdweAjsAiBzsYwfAD4ZiCewAAECUYPEEgR0AAIgSZOwI7AAAQJQg\nsCOwAxBJ2McOgB8EdgR2AAAgSjDHjsAOAABECRfbnYS6AwAAAF2DoVgCOwCRhH3sAPhBYEdg\nBwAAogSBHYEdEIumv1dkKHkoe+StgwcNTkkJSX8AoEs4Y37xRKwHtgCUN0qOPvzh5oqGxlB3\nBAA6zi3mYPy0eV6TTnvrmL6uk3eAjB0Quzbflae93nTm7PO797xfWvrd0Tkh7FKb2McOgB8h\nGYo1mUz6ub+Gt4HU6cKpw2TsAIiIzBg0UETeKDka6o4AQMe5PKZg/IT6stqBjB2Ar9yY2U//\n9nht7e4q+68OHFSHZg4cqOI/+XKW3ppbZ/1i//4RqanfHZ3jXeK/BRHZU2Xfcu7c2lOlIvJQ\n9shpA/qPSEvrpksFEI3CdoPi1nJyPtN7nUFgB0BEZNOZsyJy7/DhWskn5RXP7Nylf/tJeYV8\nmdtT3i8t/aS8YuZA3yX+WzAcfaPk6BslR1fcOGWiLT0I1wcgJjyZt1j/9t+LXm5vCz/Oe9JQ\n0oFG2kU/r67zQR6BHRC7DGtjl147UR9Uqajrl1NvGtOnt4hUNDTO/evG53fv0Qd2Q3v21E/U\nM5T4b0EdXXPrrH49kkTkYPXFx7du23LunP/Ajn3sAPjxUtGrXy9o95QzrxY60oh/fibYdT6B\nR2AH4IpTly+frqvTdjxR8dnF5ubjtbUVjU2HL170/sgEryBMX+K/hRsz+31SXrHl3Lmr0tJG\n9Uob06e3IUYEECrNi63ehQnLHd3fk/ZyRdrigS7/lyqBHRC79IHU6bq6d46fePjDzVqCTURe\nP3TY/3KK3gkJ/kv8tPDdnFGflFdo0+/uHT6cQVgg5HyGdPpDYR7ehX9g1+WT6gwI7ACIiAxO\nSVk4ZvTaU6VvHT36wg2TRGTdqdI3So7mDx0ybcCAtHhrn8TEv/vL/7WrTf8tjEhL23xXnra6\n4pPyihsz+303ZxTrJ4BQ8RPV6euEc2wX5hsUe0d1LJ4AECzJVquIqPUNIrKi+HMR+VHuNept\nvaPdX+WBtDAiLW1EWtq0AQPO1tc/9cn2T8or/A/Iso8dECSBRHVazbCN7cJ5a5I2Y7guCfLC\nPWMJoHvUOxxqYexjY8foy0/X1amjbx873rGWW2vhleLPp79XdLD6ooj065E0MDm5Y+0D4ax5\nsdUQMHnqKh2v39O82Ora+3aoeuUt8KiuY/W7jUvMwfjxf1KPx6N/dIRhMYT+hfcTJvSf7ZI7\nQMYOiF3eT4wVkWkDBqgXS6+d+PzuPQ9/uNlQQb/Awj//Ldw2ePDaU6WPb92mP/TUl+k9IFq5\ntv3SfXBd3HfetEy4P9R9uaJjUVp45u1CNceutUybVu4nFcdQLICud2Nmvyn9+l2fkaE2HxGR\nGYMGNjidajj1oeyRtw4e1OJyzf/o42L7hQADO/8tjOnT+7Vbbt7Z5JrzAAAgAElEQVRy7rxa\nXfFQ9sic3r0NOyQDUcb5l2ddG1+0zHo6fKK6KBPmc+y6QXCXZqDD7HZ7ejorBEW4FV/iPijB\nXlAWQfiVUML8PuhXkrr2vu1860HLrKfjbn+uy0/U4fvQyUHVcEva/XDtb4PR7C/yHwlGs8HA\nHDsAAILOdfB951sPmsfc6TOqc5/73LnlFTUhz/H6Pfrpd6rQc7HM8fo9zr88K7p5e669b6vX\nrr1vW5svG8+49201mU/foKfpUvNiq+NPjwfrOkPNKeZg/IT6stqBoVgA3UHN5wvGFsRPry1s\n7dCkIaPvzp0aYAsv5i/sym61R8g7gGBzn/vc+frd5jF3xn37195HXQffd75+91eVD65zH1wn\nIvrhWtfOVe6D6+ImzP3ap956UL12vvVgwlXflEXva0fVmK++QU/FwbjbnzMlplry/s1V9BPP\nbc+aUjK69CrDglPCd1Vs9yCwAxBc+iUawQvvfBrVL6t7TgT453j5Wj9HVVRn/cE285AbRMRz\nsazlhRHOtx7UB3amfmMMg57uHa/FP3Pc1DvLc7HMtXOVa+OL7qObzSOni4j76OYrM/lueFRf\nwTziFvPI6eaRM10i7qObo3KeX2Rl14KBwA5AJPHex8470dXkaPnDns0iMjpzSCBtkipDNzCP\nudN9cJ3zg+es9/7ScEhFbJ66Sve5zz01pz1lu3x8fOR0Q4kl7yVT7ywRMfXOstzwqGvji67P\n31HVXJ+/IyIqqvOuYB5wjXnMne69a6IysGvxxPriiVgPbAEElc8dVXwWdqGtx4sPV5T+3fib\ng3oWIHDWJ3fHffvX5jF3urcXunas8q7g/MuzLf880PHytc7X79aGUPW8h03NtuyvjvbOEhH3\n9ivTEtQLVeizgmXqD90H17mrSjpzUeHJKaZg/IT6stqBjB2AqFJ89tjmkr2LbrorJSFJX368\n6uz+cyd2lR4SkenZE67uP7x/Wl91SE1x+393/P2/bPiN97S8d4u37io99P/u+PtEa7xqv/jM\n8cMVpTn9huQOGpE78Cp95SZHy5HKMlVh0pDRN40Yl57SS38Wb975wuKzx97evemhSbd9Wno4\nNbGHvj9NjhafnUSYMw+4RkQseS+5D65zvrPI1He4PgPn2rHKtfFF85SFlmvmSHJfU2pmyz8P\nDGp/TIOvFRHP8a2iiw6jA0OxsX79AILK53S64M2xO1974e3dm+7JnZrV52v74R0qL121/X0V\n1YnI5pK9/7nlT8erzurrJFrj7xg7eVfpobrmRq2wrrlxV+mhO8ZOVlHdXw9/+vbuTYcrSkXk\ncEXp27s3/fXwp/pG/rBns1ZhV+mhlzf94XzthXZdgorqpmdPGJ055BvDr95VekjfQuXli8Lc\nwYhltmVbf7BNRBy//qbnYplW7nxnkYhY7/2lGieVuMRAWtO3oHJvlllPXznRlIU+K6hyETEl\npsbNKXC+s8hTV9mZKwq3vU5EpFnMwfgJ9WW1Axk7AJHEZrO1to9dXXPjXw9/Nj17wvVDRhsO\nvbHrAxH5yawHevVIEZGy6oqCbe/tP3dihO1reZGr0geKyHH7WS0Pd9x+VkRG98sSkeNVZzeX\n7J2ePeH6rNG9eqTUNNR9WnZoc8ne4X0HqHYOlZceriidnj1h6ojcRGu8CtF2njqosmv6zJy9\nrmb9gZ2qsr4DWlR3a871IjLCNjCn35Avzp/QkoulFytExJaS1rG7h5AzD7kh7jtvOt960Pnn\nf4x74LemxFTtkLuqxGzL9jRdcn20IpCmXDtXaWsj3LvfEhHziFvUIcs1c9zbC31UGD1b+7hp\nxFQRCXZqsPvVxvwGxQR2AIJL5ee6YT3sxsOficiUYVd7H8rpN+RwRen+8ycGpPYd2MuW1aef\nzwUT/dP65vQbUnzmuBbYFZ85PmnIaDWcuv/cCRFRUZ2I9OqRcn3W6M0le7UA8UhFmeqASu/l\nDrzKMFCrqLBSRO7JnaqPQVVUN2nIaBXVKd8YfvWq7e9rJ91wYEdOvyHa8C4ikWXC/Z6Kg66N\nL7o+WqH2tFOhnuOlsYaaKtTz01TLCyO+qnzjE9rwrnnkdMusp10bX9RP17PMetoy5lvaW7Mt\n2zxloXt7oXnKQuu9v+zANsVhmK4TkYaIyq4FQ6xfP4DusfmuvKBGdZ+WHtpVeujWnOsMU+uU\nW3OuE5ENB3as2v7+H/ZsNgzC6n1j+NWHK0rtdTUiYq+rOVxROm7AcHVIjeSqAEtRr7URXvXC\nZwc0xWePqajuoUm3GTKLb+/epBrRjwWrpN3+8ydERI3Jjs5kHDbixd3+3JXAa+/bImKZcH/c\nnAJ1yDLraetPD1if3C1qDpz/RvL+TUTMY+60fu//6m/+qeFo3HfeNI+5U1WI+86b3hsjW66Z\nIyKW6x6W9kdp4RnViUiDmIPxE+rLagcydgAi3vGqs38u3vrQpNu0IUuD/ml9X8xfeL72wjH7\n2Q0HdqilD7fmXOddf2Avm4icvHA+PaXX2Vq7VtIl/nr4080le0XkB9Pu9T71o1O+9bcTXxyu\nKN175ujUEddo5dcPyXlj1wcTBo2srLsoIoN6ReGmslGstQAo7vbn9JGWZfKjlsmP+vygnxAq\nbtqP4qb96Mobu91w1DLhfv8bmphHTtc3nrDcEWDeLmyjOhGp90TSCtZgILADEEm897Graahb\ntf19tdrA/2f7p/Xtn9Z3XP/hF+prV21//3BFqfeAbKI1/p7cqX8u3jo6c6hah6HGVUVk0pDR\nu0oP1TTUaUk7ldib9GXiTVWoa270TtrVNTduP/nF5pK9Of2G/N34m31m9UbYBlotcYcrSjcc\n2DGu/3DtLMP69heR4/azxWeOq6vwf5lAhwUS24VzVCcMxTIUCyCiNTla1u7/W06/IVNH5Pqp\n9m7x1qfXFpZVV4hIrx4pfZP9LT5QgdSLH7whIiNtg7VyNSb7admhmoY6EalpqNt75qjo1qgO\nS+8vIttPftHkaBGR4rPHnl5b+G7xVhH5330fq4UX35443c9YbVaffmo5hRp7VRKt8fdfO0Mt\ntjUstgC6XMJyR2uhm59D4aNWLMH4CfVltQMZOwAR7OSF82pvkX/Z8BufFVRObuLg7F2lh9Tk\nNs09rWwFl57SS+XeJg0ZrZ9RN8I2cHr2hM0le9VwqqLPFOYOvKr4zHFDhRuGjhER1UnDIX0P\nNWpBhiFpNyojy/ACCKrwD+Ba44iozYSDgYwdgAimFqK2KatPvx9Mu1dLd03PnuC9dkFPJecm\nDjYuSLw15/r7r52R02+IiOT0G3L/tTP0K1hF5NsTp2vx4vTsCU/O+HZ7R0579Uh5aNJt4pW0\nUy/6JKf6/hhiSURkzhAqptZ2hEJo2e329PT0UPciLHArFO6DYjLF4reWva7m5U1/MDxwgl8J\nhfugcB8U07sbg9Gs5+5ZwWg2GBiKBYCvGB785XO7u+6nJvOpUV0AfkRQBBYkBHYAcIX341yf\nXlsY2thO69L07AmshwXQJubYAYCIr6jOf3n3UPP57hg72TCZDwB8ImMHIJJ472PXJfxHbyHM\n2z18w20hOS+ACEXGDgAAIEoQ2AEAAEQJAjsAAIAoQWAHIJLYbLZQdwEAwheBHQC0sV9dmOxm\nBwBtCllgl5+fH6pTA4C31qI3ojoAESQ0gR1RHYAw5B3DEdUBiCwh2McuPz9/7dq1xHYAOqC1\nfexmv/euiKy/6+5Otk8kByCihSCwW7t2bfefFEC0UiGd/nXnwzsAiFDh9eQJk8mkfxukLeYj\nhd1uD3UXwgW3QuE+KG3eh9i5UbFzpf5xH5SIuw/p6emh7kIUCq/AzuPxhLoL4cJut/Mbr3Ar\nFO6DYrgP+nSd5uG/bYuFpB2/Egr3QeE+QGG7EwCRhH3sAMCP4Gbs9CskmFoHoMutv+tu76Rd\nLKTrAMCn4AZ2BHMAAADdJrzm2AFAe6n8HOthAUAI7ABEltYWyxPSAYCEcPEEo7QAAABdi1Wx\nAAAAUYLADgAAIEowxw4II8/t3DGub/qItLQhqam9EhL0hy61tJytqjpeW7v/gv3ZGyZ3pqma\n5ubSS5f0TflZeWA41OYahQArtEa/EuLlqTfn9OljqGCz2e5498/MqAMAnwjsgDCys7x8Z3m5\niPwwd/ztQ4fqD+2prn79+LEuaWrH+fO/KN7X+d4G29tHSxZPvDbZag11RwAgYhDYAWFn9tCh\nOyvKDdHY3urq2UOHrj91qvNN7awo70BTXSuQlNvO8vKtZ88aOg8A8IM5dkDYGdc3fWd5eWVD\ng1ZS2dCw92L1uL7tfhCkz6Z2lpd3oKnuN3/s1b8o3nemri7UHQGAiEFgB4SdEb16icjJS5e0\nEvV6cM+ehpr1DseWM2ee27lj9nvv/pevGCjwpsLQbUOG3JCZ+e7XB6DvePfPoeoPAIQ/Ajsg\n7AxKSRGRIxcvaiXq9fC0NEPN5Xt2v7T7MzWXbv2pUws/3HiitrZjTYWhZKv17uEj1p86pS4Q\nANAm5tgB4Whu9qg1JUfuveqqZKu13uFYU3Ikf9BgQx21PGJu9ihVbcuZMy/t/mz9qZPfzx3v\nv6m52aO68VJ887k21nviXa7NNjd71HM7d/zm1m9m9OjRLV0DgAhGxg4IR6N69xaRioYG7c8R\nXoOnn1aUi0j+8OFq3ei0QYPW33W3Iarz2ZQqiRS3DxkiIn8pLQ11RwAgApCxA8LRsNRUESm5\neHF4WlrJxYsikpmUZKijlrUa9qgLpKmBKSlB6HL7BL4RXUaPHs/eMPm5nTty09NzbbYNd9/D\nNDsAaA0ZOyAcqWHHnRXl2p/9vQK7Djc1yCuwuyEzU0TqHQ5DuSpRR0PohszM2UOHvnviuHcP\nAQB6BHZAmPph7vid5eUnamt3lpfPH3u1d4XZQ4eKSE1zc+ebUrufnL582VB+rKZGOxpad4+4\namd5+QcdGpA9/JfJhp8u7x4AhAkCOyBMDU1NFZHvf7RZREb4WsSq4q21J06oPNaWM2fUpicd\naGq8zSYiT279+HB1tVZ4uLr63RPHtaOhNSgl5Ye541878EV7P+gzjCO2AxCtmGMHhKnM5GTt\n9ZDUVKdXOm3aoEEfnT2zpuTImpIjWuHsocPabMq7wvC0NLV49smtHxsO/fTa67z3RvFe02qY\nM9eBCj6r6U0dOHBnRbkEPMHOf/SmjubcviPA1gAgIpCxA8JUr4QENbnthszM1lZILJ547Q+/\nXAY7N3tU4cxZPjeoC6Sph0eP/q9bpusHan+YO75w5qxpgwZ18kK6SrLVev/I7FD3AgDCmsnj\n8YS6D/DBbrenp4d+YlM44FYo3AclwPsQ+GBr5Cbt+JVQuA8K9wEKGTsA0aZdU+iYbwcgmhDY\nAYgktjBYyQEAYYvFE2hV9Z458X2mWnvmxqWMtSR87f+mruYqZ90Bx+XiluqtfSa+4/3B5qr1\nCbbZ3odE5PwHPh4MlTL8p0kDHohLHumz2nmv+v1va9BXM7zte8NH8b0m+TyvoWZrtGqBXJSh\n5QAPAQDQ5cjYoVXNVesvH/lZ9Wezm6vWeR1aV/3Z7MtHftZctd5wyHF5vypsrlrvuLw/wHPV\nnXipaluus/5o57stInUn/s3tvNQlTSkduygAALoZGTu0ocfg+U2V7/cY9A/6wqbK93sMnt9w\n+jXv+o7az0Sk1zW/rfn8EUftZ9ae43w2a0hiNZ7/Y83njzSe+33Pkc8aKtjtdsfuLO+P+NFc\ntb6p/E+GPgfSjdYEeFEAAIQWGTu0Ib731Oaq9a7G01qJq/F0c9X6+N5TvSu7nZdqDzyeYJud\n1P++BNvs2gOPB5g5S+p/n4jUnXipS/rcc9TPaw883lX5vw5fFIKhqqoq1F0AgPBFYIc2WFPH\ni4h+/FG9jkvJ8a7sqN0jIj0GfVf7U5V0sx6D/iHBNru+9D+7pLUwuSgErl07mETudicA4I3A\nDm1QCxoctZ9qJeq1z+HIpor/FRFr2vXan6qkTY3n/ygiKcN/2gU9FjHHpSYP+X7D6deaKo1T\nADugwxeFEAowXCOqAxBlmGOHtqUM/2ndiZeSh/3IHJfqdl6qO/GSzwjM1Xi64fRrKcN/qpbQ\nWhJs6oMpw35sSRpsqOxzUarKh3WJhL63pAz/6cW9czJuPuJ9dv/d0E+8a9dFAQAQWmTs0DaV\npnI1lmp/qhKDlpodIpJgu0MrUa9VuX8pw3/qPwLrABUmNpx5vTONdOaiEAyB72OXc/sOPwk5\n/0cBIEKRsUPb1KirWg2q1ofqN5zT1Hz+iIhc2HmLd7laG6GnZcXqTv1HS/XWxMy/6/IEmCVp\ncO8J71zcOye+z7SEvsZeGbrRmnZdFMJQzu07vJ8tQUgHIFoR2KFtKuRSm540Vb4vvgI7/7u7\nOS7vb22LkKT+D6j98Gw3FfuMFzsjMWN2j8Hz60v/y5o2sQMfD/CiEmyzm6vWu52XzHGp+gpq\n8WyCbXYHTo0uRBgHIHYQ2CEgaWN/WXvgcbVPb89RP/eu0FT+vyLiPZzqajxd+fGopvL/bS2w\nsyTY+ly3vvqz2ZeO/Mznkyo6KXnID6q25Tac+Z8OfDbAi4rvM7W5ar2z7rDhcRdq8Wx8Hx/7\nwiCoVIrOZzzn5xAARAHm2CEgcSljRcT+yQ0iYu2Zazjqaq6qO/FSj8HzvYdTLUmDewyeX3fi\nJVdzq9uPJfS9pcfg+c1V6/0vYj3/QQ/1076eJ49MG/vLy0d+1q5PSXsuKqHvDBG5sPOWlppd\nWp2Wml31pf+lHUVXYR87APCDjB0CYkkapr1WQZ6e2gClx+AFPj/bY/CChtOvOWo/tWS0OiiZ\nMuzHDadfu7h3Tr+Z5foBTZ9hXHsfwJqYeW9T5fveTz9rrX3VeOAXZe05Ti2V9Z6K1+ua3/KY\nCgBAtyFjh4BYEmxqrliCbbba+EOv4czrCbbZrUUw1p7jEmyz/a9OVQsdREQ/Zuo/ORd46s4c\nl5oy/CcBVta066J6jnw2/cad+kHqtLG/tN1UzAILAEB3Mnk8nlD3AT7Y7fb09PRQ9yKUAozb\nAs/bRTp+JZRA7kO75tjVX/jscvmHNaf/nGK7qffQucl9rzNUHjHt3YqDyxNSR9pGfk+VXDV9\n/aVzf6k88osU202pA25L7X9ra40HdUofvxIK90HhPkBhKBbhKPBs3PkPesRObAcRsdlsXfjP\n0aqjv75w/EqSuK5qW13Vtr4j/sE28nv6OjVn3qur2pY64DatpPyLF+uqtmkfEREttgOA0CKw\nAxCdvLevM6i/8NmF4//Td8Q/9Bn6HYs1xeWoqz711oXj/9Oz34zE1K923klIGW5IuSWkjux/\nzT9brCn1Fz47/en3L537gMAOQJhgjh3CTnvXvba3PqA0VO8WERXViYjFmtJn6HdEpOHCp/pq\nPfpca/hg76z71EfUuK1K2gFAOCBjByA6+Zljp6hB2KMfzjLUqTzyiz7DHtDexiX0MVTwLgGA\nMEHGDkAkYR87APCDjB2AGNVr8D01p/88cuZGNa7a5ZzN1cFoFgD8IGMHIEb1zJwpItWn3tIi\nsPoLnx3+y+Tqk7/vWIMptptEpLHmCxFxOeoulv2xi3oKAIEiY4ew0/+2hnath2C7E3RMct/r\n+o74hwvH/0fb8UREUmw3pQ64vWMNpg64ra5qW+mO+eptxqgfdkEvAaA9COwARJKu3cfONvJ7\nCSnDG6r31Jz+s4hkjv1ZSsbUDq+NUJueXDr3QV3VtsyxP+s1+K7KI7/oqq4CQCB48kSYYg9x\nnjxhwK+EYjLxrXUFvxIK90HhPkBhjh3CVCARW+xEdQAABILADuHLf9xGVAcAgAFz7BDW+t/W\nYLfbHbuzDIWh6g9Cjn3sAMAPAjtEACI5xKyLvzMZShLHLYkf8ZAlNTsk/Qmc6nnvh5kQCXQr\nhmIBIJI07V926d1R7vqyUHcEQDgiYwcA4U6f92o5uaZ+67zmkpUy+B9D2CUA4YmMHYBIYrPZ\nQt2FEIsfNldEmvYvC3VHAIQjAjsAiDzWQXmGEsf5TfU7Hrv4O1PdpnzH+U36Qxd/Z7r4O5O7\nvqxuU37j3qVflTRWNh1YoT7ScnKN/iPO6mJ1yPuod2tKy8k1dZvyL/7OZGgKQHdiKBYAIokK\nmxJGP9GkK2zcu1TL4TnOFDnOFCWOW5I04Xn9B5tLVjrOFMUPe0Aradg+33GmSPuIfJkObDld\nVL85X6tmOOqztfodj7WUFFx5vXWeu+Fsl10wgPYgsAOAcGdYG5s8dbW1/wyx29Vbx/lNTfuX\nJY5bkjh2sSk+zdNS23RgedP+ZdYhc+L65GqfsvQaa1ijaumdm3zTG6b4NMf5TXV/ndly8vcq\ndFNRXc87tsfZJouIu76s9k9D6rfO0wd2+tYc5ze1lBQkjluSkL3AnJzlri9rLlkZlBsBoC0M\nxQKIJOxjJyKumgOuSyXaW2f5ZhFRUZ2ImOLTEscuFhHn+Y36T8VlzjC0k5DzA/URa/8ZIqLS\nciLS+2FP74c95pThzuriltNFPqM0fWuqAyqqExFzclb8iIc6f5kAOoCMHQCEO32mzXWppOng\nK5feHWWeskHSb5cvF1LUrOll+FTj7sWJY5/S3pqTMgwVvEu++qxubNcn/WdVTRXVKeG/zR4Q\nrcjYAUAksaRm95j4ryIix18N0imaj65s2r8sPntRyq0f9rxzX9p9FUE6EYAuR8YOACLMlSHX\nyg/U2/jsRS0lBb3m1qjyzmvYvlBEkif/Sr31tNT6r584bknT/mWuSyVaoo79k4FQIWMHIJKw\nj52npVYtjPXkPKdK4ofcJyJNB5a7GytVieP8pou/MzUdWNGZE6lpfGophv+acZnTRaTxs8Uq\nnmPxBBBCZOwAINx5PzFWRDz9r+xIYu0/Q+XMDLPiGncvbty9uANPa02eurp+67xL744ylOtz\ncnpaB2q/XH7RY0phe08KoEuQsQOASGIdlNdjSmHavaWepEFaYdKE55OnrvZZ32dQ6F/8sLla\nZJY4bknq3Ud63rlPRJwVW1r7iOqA2jY5eerqhJEL2ntSAF3C5PG0+x9z6AZ2uz09PT3UvQgL\n3AqF+6CYTHxrXaH/lQgweutA9i788VdD4T5AYSgW7Vb7ullEku/8JC5jss9Dad916wtd1cWu\nqk8b/7ZQROIG51mz8iyZN1vSfO+G4KNywjjx+rbyeSLEAvax8xZ4Tu7i70xRGdsB0BDYoYOa\ni39umfa7NlfhtRx5TUVpivN0kfN0kYgkfaMwftT8QCqbRVp8VQYAAAYEdugg5+kix8k/+o+3\nVKAWNzgv8dp/MacMVVGgq7q45fCvVQCn/3hrlS/vfdW7MgClvVPoSNoB0Y3ADh2UOGl5498W\n+hlUddeVqUAt6aaV+k3qLX1yk278bxFp/NvCuIHfNKdk+a/szP335KQkfWUAAOBTWAd29i8f\ncR2bwvbyrSIiUt/3bnO/v17+7OfO3H83HFI9jzv7gUmkZchjjfVmqTdeiyU93ywFl45/4Bx4\nT5uVm75e2XCi2BFr1+uTzWZjmp2mAyte1adcs6PqHvJXQ4m4+8Bqj2AI68Aulv+Th/P6JrUJ\nfZ/+w50Tflz/l1lpI++1Zt2pP6R63liyt0UkbfBEc5KPC3EnT7wsklC/t1f6Av+V7XZ72uCv\nVTacKEaE869EN+M+KJ35v3g03UP+aijcByjsY4eOixswIyH3mYaN+e46H48PajlcIK0/ZVyV\nqzrtrQwAAHwisEOnxI9aICItR3h8EAAAoUdgh04xp2T1mLW2ufgF57lNhkPxOYuk9ceHq3JV\np72VEcuYYAcAfhDYobOsWXfG5yxqPvAfhrDMknmziLjsu31+SpWrOu2tDEDp2MYlbHcCRDEC\nO3SB+LFPOE8XtZSs0hfGZdwoIs0H/sPdWGmo726sbD7wH1od/5XNzXZDZQAA4BOBHbqAJS07\n6RuFTbsWq7e1r5trXzdf/sNQEXGeLmrctsBVXaxVdlUXN25b4DxdlPSNQm1fOnNKVtItv/dZ\n2bzvR4bKADTtTb+RrgOiW1hvd4IIYh12n6PsyuPCDJyni+q8yr0fKRY/fK6INH70gKGyqZXn\nj8mXT4zV4+mxUc9ms3k8hCYA4BuBHbrGpTd7B1ItbnCeNSuvtedVxA+fa+k70VX+sYoRVeX6\nhHHxQ2/o6v4C0aP3w54AdyomXQdEPRP/9g1PkbXVpHfmzI/2JtUi61YED/dBMZn41rrC+1fC\nT3gXxSEdfzUU7gMUMnYAECVU9GYI76I4pAPgjcAOndWudJ2qz0w4dBj72LWJSA6IZayKBQAA\niBIEduiU9qbrOvMpAADgH/9/BQAAiBIEdgAiic1mC3UXACB8EdgBAABECQI7AACAKEFgh07p\n2MYlbHcCAEAwENgBiCTsYwcAfhDYobPam34jXQcAQJAQ2AEAAEQJAjt0gcCTcKTrAAAIHp4V\ni66hIjY/j5QgpEOXsNlsHg/PQgUA3wjs0JV8hneEdAAAdA8CO3Q9IjkAAEKCOXYAAABRgsAO\nQCRhHzsA8IPADgAAIEoQ2AEAAEQJAjsAAIAoQWAHIJLYbLZQdwEAwheBHQAAQJQgsAMAAIgS\nBHYAAABRgsAOQCRhHzsA8IPADgAAIEoQ2AEAAEQJAjsAAIAoQWAHIJKwjx0A+EFgBwAAECUI\n7AAAAKIEgR0AAECUILADEEnYxw4A/CCwAwAAiBIEdgAAAFGCwA4AACBKENgBiCTsYwcAfhDY\nAQAARAkCOwAAgChBYAcAABAlCOwARBL2sQMAPwjsAAAAogSBHQAAQJQgsAMAAIgSBHYAIgn7\n2AGAHwR2AAAAUYLADgAAIEoQ2AEAAEQJAjsAkYR97ADADwI7AACAKEFgBwAAECUI7AAAAKIE\ngR2ASMI+dgDgB4EdAABAlCCwAwAAiBIEdgAAAFGCwA5AJGEfOwDwg8AOAAAgSsR1/ynz8/O1\n12vXru3+DgAAAESl7g7s8vPz9cGc4S0AAAA6jKFYAJGEfeE9aykAABNJSURBVOwAwI/uDuzI\nzwEAAARJCObY+WEymfRvY3z5m91uD3UXwgW3QuE+KNwHDbdC4T4oEXcf0tPTQ92FKGTyeDyh\nOjcT7Pyw2+38xivcCiWm7oP9T1/9Gy/93q99R5lMofzWCisx9SvhB/dB4T5ACa+MHYBYpo/n\nvAtVhBfjiXwA8C+4gZ2fnU1I1wHQ8xnVGSoYsncAAIPgBnathW5EdQD02ozqvqo2jYwdALQq\nBNudENUB0AswqrtiC9udAECrQrBBsfDwCQAdZZsjrJ0IB81PWb0LE1Y4ur8nAPS6O7AjjAOA\niOYzpNMfIrwDQognTwAIpfaNw3b0I+gqfqK6dtUBECQEdgCAgAQesRHbAaFCYAcgklS9E+oe\nxKr2xmrEdkBIENgBANrQsSiN2A7ofgR2AEKpA3sOs00xALSGwA4A4E9nEm8k7YBuRmAHIJLY\n5oS6BwAQxgjsAIQYQ6sA0FUI7ACEXoCxHSEgAPhHYAcgLLQZtBHVAUCbuvuRYgDQGi100z9b\nwhDPVVVVdWufACCiENgBUcXP47YShy9KmfAr7a3bUeso3+Cwb2k6USAiSTlLrLbp8RkzvFtL\nv9fjbqqsfr+foQWDur2PNZ0o6H1HqaVHlipx1hQ7L+6q27NQROL758X3z7OmT7P0zPb5cVW5\n5XxRy/mipJwl8Znfsvad7PPS7H4vTevzV1faVFm3Z37L+aKek1YnDJ7bdHJl3Z6FyeOWJ2U/\nZWinsWRF/f7FKRMLE4ctaO0yASCcMRQLxIr4zNnaa3dTZd2nD13eNU9FdSLSeHjZpa0zL32S\n73bUen/WnJiRlLOk6USB63KJz8Zdl0uaThQkDl+kRXVNJ1fWfDheRXUi0nK+qG7Pwov/N6rp\n5ErvjzeWrFCVW84Xqc7UfjTl0if5zpri9l6aj8aP/6cW1YlI/KBvx/fPq9+/2HAtrssl9fsX\nx/fPix/07UBOGjsSVjhC8lkAHUDGDogq3hPR3I7auk8fEpH4/nlaYXPZGy3ni1ImFsb3v8uc\nmCEi7qbKxuP/2Xh4maN8gwqADBIGzmk8vMxh3+Iz5eas2SMiCVmPqLcqKxbfP6/HmOfNyUPN\n1jQRcdYUN50sUKGePiWm8mSJwxclDlsU1ytX9dl5cfelrTNbzhdpKUB1aXa7PT093c+lGdQf\nWNp4eFlSzhLtoszWtKRRT7ecL2o89oo+AVm/f7GIJI16WvUWACIRGTsgyjWWLG85X5Qy8TV9\noQpiEoctUFGdqJxc9mIRubxrns924nrlxvfPq9uz0GdK7/KuefH989TgqauhTEV1KRNfi+uV\nq8VJcb1yUyb8KnH4oro9C10NZapQy5OlTPiViupExGxNi8+YkXbLdhFpOfNH/YlsNpv/S9Nr\nPr1GRXXJY5/Xl1v7TlYJSJUgFBFtCNgw/gulY4k30nVA9yOwA6KZimzSbtmuBXBK4vBFIuJu\nqtQXmq1p6fd6/Cw+TRj8gIi4Lh0ylDsu7NCOiojzwieiUl9fP+mVRrIe0eqIiMO+RVX2rmnt\nOzl16ofxg+5r16VpWs4XqXDTENUpKmWohp7djtpLn+TL1/OIMGhvlEZUB4QEgR0QtZw1xZd3\nzUuZWOidhUoctkhE1JICLXnWJqtthog0l/3WUK5K1FH5MlazJA/32YgqV3VEROXMLKmjfVaO\nz5ihTdrT83NpWoVLn+SrrKHvbvTISr1xrYg47R87L+4WkdQb1/o8VwRxrLrH+dEr7qObPZcr\nDYc8lyvdRzc7P3rFseqeDrcfeKxGVAeECnPsgOjkbqpsOLg0KWeJzyxUXK/c3t880njsFZWp\nEpHkccvj+n7D/0CkOTEjedzy+v2Lk0b9TIuBXA1lTScKknKWaJkztSCjtUSaKm86UaDmt6nA\nrl3T2vxfmlLz4fg221ELddViDvU68D6EJ/fBdXJwnUsk7r4Cy+RHv3boQJHzj4s6f4qEFY42\nH/9KVAeEEBk7IDo1HHpWRJJG/KC1Cpae2SkTftXnWxVpt2xPmVjosG+p/WhK/YGl/pu1ZswS\n3UCq9jphYDc9w7WqqqrNS1Pi++e1nC9SlVuTPG65ml2XPG55V/YypMxTFroPrDMUug+sM09Z\n2CXtJ6xwtBa6+TkEoHuQsQOiUNPJlU0nCnrN3Nda2kxjTswwJ2ZY+05OHLagpXLTpa0zLT2y\n/GTC1BKKy7vmaYtM1Tw2bd2DiCQOX9R0osDtqPWZh1NrL9QkvzYr+3D+jUAurdfMfebE/nV7\n5jedKIjrNbG1K9JW+La2u14kMo+42fnmg56LZabeV7Kqnotl7oPr4h580729sKvOQgAHhCcy\ndkC0aancVLdnYeqNa/XBlp79Tyaf+xir3Ym1nedao4IktWBC/WkIm6zp00RETVzzpspVHe2F\n94IMEXE3Vdr/ZNInEVsqN0nJk34uTRPXK1cNHKsraqnc5L9+NDENmiAi7nP7tRL12tTPOJHR\nfe5z50evND9lbX7K6lh1j2vv2/qjnqZLrr1vO1bd0/yU1fHO4+4q4xaG7qObHe88rj7rPrpZ\nK3esusfxzuOGplQjXXF9APwhsAOiiquh7NLWmUk5S/zMGFPhjnes43MfE29x6TeLSEv5+9qf\nquSrCn1vFJGmY68aVt2KiLupsunYq1od7YX3ggwRaTn/nojEZ35Lf2mS9WTgk+EsPbPVnimX\nts4MfI1IpDPbskXEU7ZLK1GvzQOu0VdzHXjfseJaV9FP1Fv3wXXONx/Ux3bOtx5xvvmg++A6\nEXFvL3T861j3uc+/OrrhWUfBN1UK0H1wnaPgm84NV0a9LTf/0L29UF/ZU3FIRMyj/e0jDaBL\nENgB0cPtqK3f9/34/nlqR7rWqMDo0taZLZWbtGDOdbmksWS5iPSctNr/WczWtORxyxsPL3PW\nFDceXpY8brlhFNXSI6vnpNUt54vq9szXPzrCWVOs1uGmTCzU1l5YemQlj1vedKKgbu9jWmV3\nU6X24C+1nkO7NNukl9t1T6x9J6srqt/3/QAj1yhgmfW0a+OLnqZLohJvG1+0zDJuKON8/W4R\nsf5wm5oYF7/kuIg433xQHXUdeN99cJ1l1tPxL1xIWOGIe/BNEXF98mt11H10s2pTHY1/4YI6\nowrmzCOnm8fc6S7+k3Yu98lPRMSUMTLYFw6AOXZA9HDaP1aLTKvX9vJZQe1RZ+mZ3XPS6su7\n5l3aOtNQQf+EBj/UEgq18lS9NlCNXN41T9sBWOP9JNak7KfcjprGw8u055t519QuTVp5Hq7f\n7ffmOi8daDy8rLFkuc897aKPKWuSiHiqT5kGXOOpPqWV6KlJcp7Lle5zn3suntZn+ETEfWi9\niFhuetyUmCoilgn3Wybc/9XRYx+JiGX6U+qoKTHVMv0p18YX3SUfqryg5eYfOgq+aZn8qJrn\n5yr6iXnMnSqVCCCoCOyA6NFSvj7AmgmD58b1vdFR8YFaEyoiSTlLrLbpappdm9QSipbzRYZl\nE8ZT9JrosG9Rp1D7iVjTp/lcppA89vn4zG+1lL/feHjZlZr9btPvKhf4pfmk4rnGw8viUscG\nErlGOvOAcSLiKftUBlzjKftUWsmWOTc869r4os8W1BirqafvFSrqUy3P9DWWF/0k7pYfyZdJ\nO1fxn+Ju+dGVNN7YOzt8OQACR2AHRI+UCb/SP/zUP0uPLMuwBf6fteAnDaZ2923jFD2zLT2z\nA3ycg7XvZGvfya1l1HSXZvLTK2m9z8ljn/fZuP/WIpTKk7kPrLNMflRtfeKdLXPtWOXa+KJ5\nykJL7hxJ7mvqmdnyzwO7sA/myfOdr99tufY7aoKdKev6LmwcQGsI7ABEkqqqqlB3ITLE3Vfg\n/OMi97nP3QfXWfL+zbuC2q/YOueX6q2akKcxT1no3l7ouVzpM2mnjsa/cEENxfpkHjFVRNzH\nNrv3rBGvpRsAgoTFEwAQhUz9rxYRx4prRcQ8sNXncKhNTDxNl1ybV+jLzSNuFhHXtl+qgM+1\n9239fiWW3Dki4tq8Qnt2mfvo5uanrM6PXvmqA4mpcQ++qdbVei/dABAkZOwAIAqZ+gz76nXm\nWO8KKupy/KvxkLuqxGzLtky4371njWvji/pJeJYbv6demEdOV8tg9UfNY+60XPsdfVPm0Xd8\neYiNToBuQsYOAKKQqWeGecydImIec6fP4VTLhPvj7ruyDNky62nrPx2wPrVbRDzHt6rCuO/8\n1lBBP5wad8dzcQ++qT2mLO6+grhv/9pwIm2gVh9lAggqk8cThROHo4Ddbk9PTw91L8ICt0Lh\nPigmE99aV4T/r4S7qsTxr2PNUxZqM/mCIfzvQ/fgPkAhYwcACAr3Z2+JbgAXQDdgjh0AoIs1\nP2VVLyyznmY9LNCdyNgBALqYmt5nyfu3uDueC3VfgNhCxg5AJGEfu4hgffTPoe4CEKPI2AEA\nAEQJAjsAAIAoQWAHAAAQJQjsAEQSm80W6i4AQPgisAMAAIgSBHYAAABRgsAOAAAgSvDURYQ7\nng0KPX4fYMCvBKBHxg4AACBKENgBAABECQI7AACAKMHUBAAAgChBxg4AACBKENgBAABECQI7\nAACAKEFgBwAAECUI7AAAAKJEXKg7AHxNfn6+9nrt2rVt1vFTDRGtvb8J/BpEPb4cgEAQ2CGM\n5Ofn67+IDW/1+L6OboH8JgT+24IowJcDECCGYgEAAKIEgR3CSCD/1CYxA8QgvhyAADEUi8jD\nzCoAPvHlABDYIUwFOIeGf6MDsYYvB8APhmIRYfimBuATXw6AkLFDCPkZNOGf2gB84ssB8I/A\nDiHjZycqP1/cfK0DMYsvB6BNDMUivLTrq5nvcSB28OUABMLk8XhC3QfgCsOu8aLL6um/pln4\nFgta+6/Mb0Js4ssBCBCBHQAAQJRgKBYAACBKENgBAABECQI7AACAKEFgBwAAECUI7AAAAKIE\ngR0AAECUILADAACIEgR2AAAAUYLADogVJi/5+flr1qyprKz0rtaZE61ZsybAFoqLi1euXGko\nXLp0qZ+PB9J4ZWXlypUr1YUsXbq0pKTEu4X8/PyioiKtsLa21mQyPfbYY/qaK1euLC4uDuRC\nACBceADEBj/fA/v27TNU6/BZVq9eHWALR44cycvLMxR++OGHfj4eSOMVFRXeF3jkyBF1VD1m\nat++fdu3bxeRtWvX6lvW3wclLy9P+ywAhD8ydkBs0f/9r6ioKCwsFJGlS5fW1tbqK3Ss8ZUr\nV86bNy/AyosXL37iiSf0JWVlZTNnzuxk4++9956IrF69Wl2IitheeeUVrRERyc3NnTx5sva2\ntrZ23rx5ixYtys3NNbT2xBNPLF68OMArAoCQI7ADYldGRsaCBQsKCwuLioo+/vhjVagfitVe\nr1ixwmQyrVixQkQqKysfe+wx9VYLB0VEDW4eOXIkkFOvWbNGRGbMmKEv/PnPf56Xl+ezfuCN\nqwHWuXPnqrfqRUFBgf6oofKGDRtE5JFHHvFuTfVQ9RYAIkB3pwgBhEhrf+VLS0tFZMmSJd7V\n1Ovly5dr3xjbt29ftGiR9rawsFBrR0uStfndUlNTI7qkmqIGSdUIqffHA2/cm4gsWrRIvVaB\no1auxoLz8vK8B4X1pxaRmpqadp0UAEKCjB0Q67KyskRk2bJlrVVITU31eDwq5JoyZcq0adO0\ntwsXLtSqaUmyNqns4OjRo7WSsrKy/Pz85cuXqxFSb4E3bqBWP9x3333q7YIFC0Rkx44dqvyJ\nJ57YtGlTUVGRYVBYT/VTy2gCQDgjsAPQhrvuuktEtJDrjjvu0L/tALVMtX///lrJ97///by8\nvPnz53eqo14qKyuXLl26ZMkSbcw3Ly9v7dq1U6ZMWbp06dq1a2fMmPHqq6/m5eVde+21PlfL\nav00LK0FgPBk8nR0ljSAyKJmy/n8K68/1NrrNt8GciKfFVauXLlw4cJ9+/aptQv+P95m45rK\nysr58+fn5uY+//zzrdXZsWPHlClTVq9enZycnJ+fv2/fvsbGxilTpqxdu1Y/2y/wkwJAaJGx\nA2KdykXpJ9J1MzWeO378eJ/rNjqmrKyszahORH7729+KyB133OFztSwARBwCOyDWqZHHCRMm\nhLojXWbHjh1DhgyZNm2a/6iuuLi4oKBg9erVaWlpPlfLAkDEIbADYpd6QsPixYvz8vIMO48E\nlcoOak+8MCzp0hd2oPGSkpIpU6YsWbLkqaee8l9T7YGipgwatlnRv1X9DGFGEwACR2AHxBb9\nI8X69eunhkG7OWrJzs4WkfPnz3dVg/px2zfeeENEli1bZnh+muEjJSUlBQUFy5cvT0tLE1+r\nZbWaqp+qzwAQ5gjsgNi1aNGi1atX19TUdHPUcvPNN4vIoUOHgtG4n31b9NRgq7YNivdqWa2m\n6qfqMwCEOVbFAgiBNWvWzJs3LyK+f0wm0+rVqzu8kR4AdCcydgBCYO7cuXl5eZs2bQp1R9qw\nadOmvLw8ojoAkYLADkBoLF++/NVXXw11L9rw6quvsmwCQARhKBZAyBQXF+/atUstXAhDK1eu\nnDRpkto2GQAiAoEdAABAlGAoFgAAIEoQ2AEAAEQJAjsAAIAoQWAHAAAQJQjsAAAAogSBHQAA\nQJQgsAMAAIgS/z8LGSUE9T5MHgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_ind(res.pca, col.ind = \"cos2\", pointsize = \"cos2\",\n",
    "             gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n",
    "             repel = TRUE # Avoid text overlapping (slow if many points)\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要在图上创建观测量表示cos2的条形图，您可以使用fviz_cos2()前面描述的变量函数，通过choice选择类型，变量是var，观测量是ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JJXitTO/K4PD0NsM6DlEQnASl4nUnIbfObOnYchthnQ8ogEYCWXv7Ohu1vXJr8b\nh4chthnQ8ogEYCV5r50AtDwiAVhJRBKAlkckACuJSALQ8ogEYCURSQBaHpEArCQiCUDLIxKA\nlUQkAWh5RAKwkogkAC2PSABWEpEEoOURCcBKIpIAtDwiAVhJRBKAlkckACuJSALQ8ogEYCUR\nSQBaHpEArCQiCUDLIxKAlUQkAWh5RAKwkogkAC2PSABWEpEEoOURCcBKIpIAtDwiAVhJRBKA\nlkckACuJSALQ8ogEYCURSQBaHpEArCQiCUDLIxKAlUQkAWh5RAKwkogkAC2PSABWEpEEoOUR\nCcBKIpIAtDwiAVhJRBKAlkckACuJSALQ8ogEYCURSQBaHpEArCQiCUDLIxKAlUQkAWh5RAKw\nkogkAC2PSABWEpEEoOVdiZQFkth7hAUBEEkAWh6RAKwkIglAyyMSgJVEJAFoeUQCsJKIJAAt\nj0gAVhKRBKDlEQnASiKSALQ8IgFYSUQSgJZHJAAriUgC0PKIBGAlEUkAWh6RAKwkIglAyyMS\ngJVEJAFoeUQCsJKIJAAtj0gAVhKRBKDlEQnASiKSALQ8IgFYSUQSgJZHJAAriUgC0PKIBGAl\nEUkAWh6RAKwkIglAyyMSgJVEJAFoeUQCsJKIJAAtj0gAVhKRBKDlEQnASiKSALQ8IgFYSUQS\ngJZHJAAriUgC0PKIBGAlEUkAWh6RAKwkIglAyyMSgJVEJAFoeUQCsJKIJAAtj0gAVhKRBKDl\nEQnASiKSALQ8IgFYSUQSgJZHJAAriUgC0PKIBGAlEUkAWh6RAKwkIglAyyMSgJVEJAFoeUQC\nsJKIJAAtj0gAVhKRBKDlEQnASiKSALQ8IgFYSUQSgJZHJAAriUgC0PKIBGAlEUkAWh6RAKwk\nIglAyyMSgJVEJAFoeUQCsJKIJAAtj0gAVhKRBKDlEQnASiKSALQ8IgFYSUQSgJZHJAAriUgC\n0PKIBGAlEUkAWh6RAKwkIglAyyMSgJVEJAFoeUQCsJKIJAAtj0gAVrKESP9dQuve/x4J0PJZ\nwNNOEHeKsiJ1oXXvQwFanhkJwEoikgC0PCIBWElEEoCWRyQAK4lIAtDyiARgJRFJAFoekQCs\nJCIJQMs/lkhZIIm9h2AdACIJQMsjEoCVRCQBaHlEArCSiCQALY9IAFYSkQSg5REJwEoikgC0\nPCIBWElEEoCWRyQAK4lIAtDyiARgJRFJAFoekQCsJCIJQMsjEoCVRCQBaHlEArCSiCQALY9I\nAFYSkQSg5REJwEoikgC0PCIBWElEEoCWRyQAK4lIAtDylYmUBZLYe4w+BIBIAtDyiKQHACJN\nAC2PSAvEeeIAABT4SURBVHoAINIE0PKIpAcAIk0ALY9IegAg0gTQ8oikBwAiTQAtj0h6ACDS\nBNDyiKQHACJNAC2PSHoAINIE0PKIpAcAIk0ALY9IegAg0gTQ8oikBwAiTQAtj0h6ACDSBNDy\niKQHACJNAC2PSHoAINIE0PKIpAcAIk0ALY9IegAg0gTQ8oikBwAiTQAtj0h6ACDSBNDyiKQH\nACJNAC2PSHoAINIE0PKIpAcAIk0ALR9NpCyQxN6D2AOASALQ8ogkgCT2HsQeAEQSgJZHJAEk\nsfcg9gAgkgC0PCIJIIm9B7EHAJEEoOURSQBJ7D2IPQCIJAAtj0gCSGLvQewBQCQBaHlEEkAS\new9iDwAiCUDLI5IAkth7EHsAEEkAWh6RBJDE3oPYA4BIAtDyiCSAJPYexB4ARBKAlkckASSx\n9yD2ACCSALQ8Igkgib0HsQcAkQSg5RFJAEnsPYg9AIgkAC2PSNcCSew9yu8AIJIAtDwiXQsk\nsfcovwOASALQ8oh0LZDE3qP8DgAiCUDLI1JxIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1Jx\nIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1JxIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1Jx\nIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1JxIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1Jx\nIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1JxIIm9NdgOIJIAtDwiFQeS2FuD7QAiCUDLI1Jx\nIIm9NdgOIJIAtDwi3R9IYm9PsgAiCUDLI9L9gST29iQLIJIAtDwi3R9IYm9PsgAiCUDLI9L9\ngST29iQLIJIAtDwi3R3INpAGIjkDtDwi3R3INpAGIjkDtDwi3R3INpAGIjkDtDwi3R3INpAG\nIjkDtDwi3R3INpAGIjkDtDwi3R3INpAGIjkDtDwi3R3INpAGIjkDtDwi3R3INpAGIjkDtDwi\n3R24QwlpIFJRQMsj0t2BO5SQxq1Fan9DPk5/N8Qddvz2gJZHpLsDdyghjRuL1Pb/Gx6nv0vi\nDjt+e0DLI9LdgTuUkAYiFQW0PCLdHXBQwigQ6RpAy7vq3hiAgxKWm7ZRpOYU/11C2+h/jwRo\n+SzgaSfqAByUkAOKiaTF3nf1ATyVEAFApNoBByVEABCpdsBBCREARKodcFBCBACRagcclBAB\nWP7OhjZ9PPvOhmJFARQEHJQQASj5Xrul2wS4J+CghAgAItUOOCghAoBItQMOSogAIFLtgIMS\nIgCIVDvgoIQIACLVDjgoIQKASLUDDkqIACBS7YCDEiIAiFQ74KCECAAi1Q44KCECgEi1Aw5K\niAAgUu2AgxIiAIhUO+CghAgAItUOOCghAoBItQMOSogAIFLtgIMSIgCIVDvgoIQIACLVDjgo\nIQKASLUDDkqIACBS7YCDEiIAiFQ74KCECAAi1Q44KCECgEi1Aw5KiAAgUu2AgxIiAIhUO+Cg\nhAjAjUTKNgtwL8BBCREARKodcFBCBACRagcclBABQKTaAQclRABuJBJBxApEIogCgUgEUSAQ\niSAKBCIRRIFApOrD/pLSZcStG3iMsHbzViJVcWizO7F9L+9wnDJf97sA2LwFD5E/DJtauJFI\nuS9rPkObgc0lbGth+15uL3JBFDDJBop09vbIHOncs7cdppst7RYc3FuKdDos2wdpiZN5VsZN\nRbTZL5hfsg0j3y5poIBI23fCbGKR61tqvN01UoH+zZ7N9fzmfuu2sRXYbJK9m+2iGjaIsGgL\nS47mLQ9Dtolzamtn7iPS5gVDBjg1r27iktAP/tLZ6tbLouyMsmS9km8hU6WV/jvO5rOLiLRg\nJ9Zv4uLR1s68s0j95szu2QAMKfvItdYoXdpvCpcFBio3SjNrt6N+Nu5/m91EbjcyZyy9/XNp\nSwbgtlFsHYbsJsxD0D/bbiG3m+VFatt+AOeOv9F/lyb08XP5zzh0mU201iBqc4c2C5xT+k6k\n27Y9UNtInmbWoG+jP9Pkr8PmR3F7VFOTreQWXmsPQ5c+WulhI1rjxpBtF+zmLWak1jz2A5a7\nHaA2cDku9qHLHLjhmGnHbvIoTS8z6Wiv3YYizfPBeYN2kfrmj9ZuLjil9U+1zxjGs7vNbDSp\nb0uvIX+k9d42TxfDJrS4xTXSULPdeXphwwDT54Nl148zQHs5wWQWPdayadRp9nLAOvz9KVZt\nYlKNVoO2BWsmSAu0eyK7LNNON0kmc1ZVG0mfuXY8mWfN8UnVEsk6EIVFWrZktifay1Q6sBox\nv6VWgvNAN3qMk9hce3Ml6s83z7TDDmQG0Gzvtm27rIajcsLpzyfaFi4N6EO8FaSy+czUPaiq\n78QFyZ5Ws8tHxaPMkE1yKldUJHGuVymzon6ivfxDyyurheG3nWyzmx+m8ymwqM5FM5o9YfTD\nY4XLXfP2vNwnzfPJcR4Y/dY80lqRUg8FWaJJejRni1R2QLajPNs2qVUep1FWpPP/MyZZJ9Gh\n541zjC3iYOF814zGRsZEa2Hfqax176UUbSe6CszIKNafys0Zbb47lgyg/MLTXK9Ntq11R7YG\nZUCNDm7mTKCdNa1zTddqrptOUVKk8bk+h+k7fllPKGu3JRfo+vkt3W5+fMyuV9IN2Z519Lyu\nSvsDYCkylJKd0TLrfvOclz8P2DcRjuklvHoYcjW0AzlTnlnk2CS1BbUzct3UR1GR0q2bh3dE\nj3PDsdFOBrlzade92iCceTRt37xRog+MSZnKTgwLGuPZ6mFMZ8z8OSszMduvIZlE5kgnO6As\nzdJluNqC9uTj6BhnZ339QJoD6rKyNBcnp7iJSJkbRebpqU9mpzQjbQ3S4ciuGx/pkdU10G+G\nJG1kitRP1u1QoiH8qJ1R7riks/rWLdvtncidbobGjZOiNRqSndSmuwWaqBVcnt9NW9a4u69I\nXWFzRaXdNgcke2N2nHYKnjxbnewWmmieiZWd6IC//zKTtnUyaXM37Mx9GCUy04m9dFuwJrM8\nzIg2XOuaM7cCjBcOxgIjaWmmcXsn+ygl0qK+MQ9s/9vhdD3Ot+P/pg20be6YJTOifuBsE/X2\nJ82YRGttpweMFsyN2FPeaP/Nc721DaXxUQk2ZXZXMsjtZbbV+DCj2TVoM5pdYBqFRBKzqAUp\n6TbllKebxyWzGhp6Vb3btuAkvcikzPAR1Rp59ZZlvvmjNeWlBdqTplVBpoihOwwmV4K5l93a\nxWr68tic9pQahll/iUnFlnb5WbRP2CLph88WMT1wxsaz50cdyao63lJ2VrMAbQnbJqdpdfu5\nKW+YeNfMielT9Br6g2WuqbLrupwBWhvjPphZ34xR5bSaHTNDlLtGuuy3eYpQz7OpSJZJY1RL\nzR9agc63MAwADbDa6ARQy+zPc+ZxsoBOstwZQdmP7Bxx2YnW6Ktkw/rcPVwPqzVmTbK2kMyZ\n+uLCHo3DY3WB087g81HwZsOSBY9+7HLjPMnpZ8l+7WVtwNRE9yR3jut+3WZu2B27o6CNQb31\nfsO5u2ndNtSnm80nHit91Y8v/Wxx+bllcs+fL9STXnpaV05I7fxjQanFySgg0kIFhqGuAHYr\n6S097enWgcuINHrj3dz4yJ7jOqhbmSlDMHeazQGZm/NpGeqBMg9Tcrd39pZPaqJ1MtBK6M+F\n86bPbUbdjWFbc0BfpdqCmh5fRSzRabtISa/kJxP7BHP+qWatEWofuOw03d2CMGrMqJqMjwWH\nQV8SWXNil7EOdn+utw7UaFPTEpKtTctLNp7ry/kTUjsUYU029oDq93B2VWbtvyxypoWhi3Nn\ntSFKzEjJOW67ScZiQs13k4C+6dFpVNu6UeGlywxVR+PDGOTGMD3/WgWSSX3+yV3WuDBZMsIs\nkUZQZto0p4LcbGLPSf28rVzdZE/rQyGzvT16tMijItdIwwDMdOAYn8PUFs7/qc1n+nXUeXrv\n6Ue26zdrC8OT1R20tn+Ux2m2RLuB80Za5dJlycIh2cxc0+PGlGe39lXi4IkKHI/LJnY1ZZ2T\nZSEmsdSjrSLlh/DxilPE/NVxbimQLGbMGkxThzJVjzIbGPZRHT/djGJXmTMpMxGos3NuhA01\nan3apg8ys65xpPurSG3WzUzs5i4Mc5UCpb+cLzLZTXuRlcYmkdpu2FinlvxEndlCZimQZNad\nw9oE0dYKxwWqHvXxMS5hs0l61uiQ9AjmZmVlJ9IjZRVozwWtPtmMVwX6CUk/eQ8eqdPN2CS9\nTrszZWwTqf9hrMgWTNTZLRhLge40n/5DaUHZxOhmiT4CF8wG6mSQKaGrQ0dGe5E5XyijOD1O\n1maMMpaIJMhpCVb7ozlTl806CkNK2UT+D/RHZ9Z5ZBpbROqXKupudQclN1Ev2IJydhgIrW/E\nnab5JoybJeNzrCqadT45Zk6jl1b0KsVeTJBEdeU4547TqAGjRvFgUsfY15nn94VozXfXWJkp\nR+2K/pm5WS8zq+n52dgkUlqYNUnaCxJrCxcD7SubrAdW+nhMjqs6Bo2+6RvOy2ocpWEyM2rI\n7ORo7pxDrCZ6ZolJGtBLrm7AmA0Gj9r5DwlIn5eb8qwiMx8SkG9hLgqIZJSUiLTGo3R4WYy5\n46YmSxZsyTO1ITrUaJzjtM5LnrtgCKua5Pohd5ySrZgH29iMPa2az+ySwznp+hLHvZQdMfPD\nIdnBayakAiIpm0x35pqLNtFKZp6+MJk2slmj48dTltpE21rr6Ysq9nTVHuf3JSv7AClU14I9\nitUZU2L2eScD5Gs4mjOePuWN1wXTDY2G61yR7fD7zG7OxFqRxn2r7VZyHl25HWsLo01ZzzXP\nj5lhmqRz7cwvurrZ4GiPj2TWmrSwzKT0h1Kh/fRFJqk9sfAknp2TlG30eW1X0nWtsvqYNCOA\n49DC1SuolSItOFO33dlpu0XqNGzPFNazk18vNSl7np3vvNTEaX740c5PWeaqcIAyl/nLdjKz\nFWPz/djNP3uBSfo+9INKe2ZuRC5Irxuu60Qanxj0cdqdJLaY1GpXnu0w060yqf/lstXdolPp\n3K/brhF12ZYfH9ZGuiNtH4TMTp7/W9lP/Qhc9CpH9kCum1WTZyqjRfT4HJEvUItVIg3Dwpxw\nSkxFRjPpAF9hUvIr67i2uetnq4nhRKLebkvuU+hF2udK61QqdlLbizb3p+2ZGErY2uXGPcXR\nlqxnqmcsc+nYzjxaHquXdkvELSOS3vwxY7L55DZjUjLbLtmAsdqwKmzNE3k/4Zr72I8w83Sx\nYMJaL1Jysl/dhGxJSSjnoySjnNTMJdRoOr6jSC5MWjhZaM8dzUk2snI/hhbMBUm/CFbT2qXm\naADNT6t5k7oZc313bXnuNZu5bExuqR0PhPlK2vRYTrPpHtxvaTfZtEmVj+HSaG3z1iFPN7Px\nPGvez00zyrpvNM9MiGEAadtYMj76raw+mgvvnG+JNt3XiUgJZpwvbM+SZ67akWtFWlb0cUSV\njtY+Jte1tCF9xQbmlyPJZGWtDNXGk6aM5ak9PrYfyU0n8oWbGGQ/TnYkPataV+x2hbkZLRdX\nizReKdx+PldK2Nb9S2+clzLJXLhltp2/ZjAGUHKYtAlp9OPK6HdumDFuEWIH5kTqlmzqCSln\nUnfWWjsvX720y6+5bxz9iXpmkl/cxH1NMiYVdSfaZJAatWW6vndNvxemrz2zMXTF+r5YtJ3k\nx9wlUvdbZdHW5o/l9p6+/hpp2zXZ5kg6b/XfZqQH/Q4mmSsKJZ2u68yLG3tdsGAv14vUj1Br\n1t0Wo2nmcuJQ69BPSclrDNO0+LkyVtxs2HRNtj2GI7vyNHhdv5e7TlLTM/nxYkY1yRhAmTsA\ng6eWq2aMT2Q3Oa129S2U3Zh0/06788l2fLBXxjUiiTPQTiLlTkD5p2948k1Cm5EWVzpn4lG7\nZT5qv7u+WudRYvrNPBqubbKbsK8m52tMMvcSKVkIb7gmKxGbpsQ22YViFd0gFl/G2es2a3Ct\nnYqGjWrX/2VivKIzK22195Fd5iLt1J++kLJtPFwhUvpj30G4aVGeXKM7dWl8ZbR2AA2EtZWN\nPXnL49deM9SsmynWErfYpLpYpETd/cNc/i948s6XeXa0C16nT+BtC56NJt10mbz66k22YTYz\nzKp3EqnbmBOTNpTh42SgxbBe33S6SBqc30ruDy+Wt32zw7lttCXLlvmGytxluMS1IhXb8MZY\ndXm85cn3iq5zu9sAN9tKEUtvPB62ny71k4V57XR9XClSse3uEMtfht0xBo8KzUfGVgq0f8Ma\nz+1vaDy9F6fcryuwlS4WiTS+p+F2EOaiTe/S7FyLGoM/Nyyx2Dr95vdrNi3t2q7HZ9d1lx+F\nJtUlIo3fReB3DOYifTnB715sfJks3/rRWvCsau6WsdkkRfbk5u2xxJFYIFK3pZKXZneP/hzv\n9653Fze8pdifEIupevsjub6zjHVnMlsV2oW8SO0DjUE1+hOx8+noL263sktut9z66mbPSE5F\n8/s4upNzJ5Fuuc64Y3QmPcR+3GqUp9cFD3tOzMfw9rnj/HEc7/t9RBqO/WMfd+NzlR3GLd+7\n9sDriqXRZt7vfYt32y6bkVy/o2ZZtG5eTF4Stym09HWB2+h21FjXXR4VG9O2SOmFxfGxD//5\n6D3yHhSI294RdBT523WXx8UOhCnSQ11YaDG68nzsXSkX1R+G3C3m4gcgs7R7hJtcdogrz0fe\nlXIR4DBkdrH4xLzoZsNDH/fMlWe0KHld4DrsnSz+mujSmw3ltnj3MK8840UMjY6ZQVt8VGsi\niduDD33wjStPImgUfy1EFamdPnrYeOg3NxG3iOLnVXVpV6FJBNFF8fWJfo3Ub6mGUVjDPhAl\no/SAmBVp/FdlVYxBTCJuGjMind9r3xb9Q1yCqDvmRDr/v+0+Y/DOFRHEA8ZUpH5F9/C3vQni\nbjEjUv8AiQhiYRgi1XGXgSDuEYhEEAVCv0ZiaUcQi2Mkkvj09h3KIYjHjFSk9ppPbycIYohE\npOGP+Xn9iCCui0Gktndot2II4lGjF6llLiKI1TGekW73GZ8EUXWMrpGCfFYTQRQPcdfu/HOX\nSgjigUO8jvT3/30qIYgHjvE7Gx7p87EJwlGItwhxz44g1oR8rx0eEcSKWPxlzARB6IFIBFEg\nEIkgCgQiEUSBQCSCKBCIRBAFApEIokAgEkEUCEQiiALxf+jbnwU+OzxcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_cos2(res.pca, choice = \"ind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要想描绘观测量对前两个主要组成部分的贡献，请键入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mog2XD4gACRieCUQbLh8AEBIhPBKYNk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\nFIPEYmUoBonFylD/B21E/0DP6oE2AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total contribution on PC1 and PC2\n",
    "fviz_contrib(res.pca, choice = \"ind\", axes = 1:2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2 观测值自定义作图  \n",
    "在这里，我们描述了如何按组对观测量进行着色。此外，我们将展示如何按组添加浓度椭圆和置信椭圆。为此，我们将使iris数据作为演示数据集。iris数据集如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 3 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 5\n",
       "\n",
       "| Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "\n"
      ],
      "text/plain": [
       "  Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1 5.1          3.5         1.4          0.2         setosa \n",
       "2 4.9          3.0         1.4          0.2         setosa \n",
       "3 4.7          3.2         1.3          0.2         setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(iris, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iris数据集包含150个数据样本，分为3类，每类50个数据，每个数据包含4个属性。可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。“Species”列将用作分组变量。我们首先计算主成分分析如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The variable Species (index = 5) is removed before PCA analysis\n",
    "# 第5列不进行PCA运算\n",
    "iris.pca <- PCA(iris[,-5], graph = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在下面的R代码中：参数habillage或col.ind可用于指定因子变量以按组着色个体。要在每个组周围添加集中椭圆，请指定参数addEllipses = TRUE。该参数palette可用于更改组颜色。要删除组平均点（组的重心），请指定参数mean.point = FALSE。如果你想要置信椭圆而不是集中椭圆，请使用ellipse.type =“confidence”。置信椭圆是对置信区域的描述，浓度椭圆是对点分布的描述。置信椭圆的算法复杂，背后有很多繁杂的数学原理。置信椭圆的长短半轴，分别表示二维位置坐标分量的标准差（如经度的σλ和纬度的σφ）。一倍标准差（1σ）的概率值是68.3%，二倍标准差（2σ）的概率值为95.5%；三倍标准差（3σ）的概率值是99.7%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dxb1r03NLxv/1QXUsxRoiRA4mYfkvt2\nbcbd5dx7O8N7GXEgJUkCJG5SkPIdAVJfwNEQ0owjQFokQBKqzGRDyBEHUookQOJmH5Kz38Od\nQQo6GkCadQRIS1QBpOF+D/cFKeyIBSlBEiBxqwHSoLuCFHF0g0RwBEgLBEhC5Q8fc8SDxJeE\ng+hzAyShsoePOrpCIjkCJPkASajc4eOOmJDYkgCJGyAJtQwkoiNAEg+QhMqcbJhxxIXElQRI\n3CqDVFCSaUhzjs6QyI4ASTpAEioL0qwjNiSmJEDiBkhC5SzYeUcnSAxHgCQcIAmVMTzBER8S\nTxIgcQMkodKHpzg6QmI5AiTZAEmo5OFJjhIgsSQBEjchSAUc3SkkmqMeEtMRIIkGSEKlLlhA\nqjNAEipxwRIdHSBxHbEkARI3QBIqbcFSHQGStQBJqKQFS3b09yffESBJVhukcpIMTjbQHSVB\nYkgCJG6AJFTC8AxHgGQtQBJKGFKKI4YkQOIGSELxh+c4AiRrAZJQ7AXLc/QbkGwFSEJxFyzL\nUSoksqRFIbXgCJCkYi5YpqP/AZKxAEko3oLlOQIkewGSULzhuSukVEhUSYDEzRQk95zMdwSJ\nvUICJGtZgjQ8Wv5dQWI6AiSD6UFyVz8XR4ML/ZCKSbIDKcERIFlLBtKco9UYTXuQGAt2SUhE\nSYDETQXSauWTdK+QUhwBkrU0IK38kGjbSM1B4joCJJNZgkSatbt7SKe97BqC1IQjZUjRq9UN\niTp82goJkKyluo0Uv9ZdQGI7yoVEkwRI3NRm7WauAUhRR4BkLR1IpO4BUuoKKQnSv8cASSRA\nEoq0YJMdAZK1AEmoiiHtjokp+g+QRpWFNNluah9SuqMMSPMbSYCUkBlI02m8AKRSkvQh8R0B\nktmsQPJMiNcNiTB8xgoJkKwFSELND5+zQsqY/rYGqQ1HgCTV7PAJjhiQ/j2vfgKQdiEsu2uA\nxIoMaT29CNtIOcNnrZByIPWSAKl0VEhrQUgnQdRZu0ogzS3YvBWSMKQl30e6L0hrwTVSaLe7\npiFlOgIka4m8tOM78kkCJECqJy6kru/XTKvvjM6QGDeY+/Mmii+kzU9+u9+MzpC8v9v++XOG\n9GdS6HK5drzlKgUhN6yRhIoOn7I+co+bnzH9HZ3/xhopMX1IjW4jVQrJ80wXCJCcCkEKfTqp\nXUj5jrIgESQtB6kRRyYgBQpBKiQJkGjPdIkAyQmQIkUWbAFHgGQtI5B8r+4ACZDqSWRfOzYk\n73xDq5CSHAGS8UxA8s+AA1LYkTQk4f3tAMkJkFKGL7JCAiRrAZJQZSFNTmLeCqRWHNmA1OI2\nUmj4MiukPEjUz/YBEj0bkBqctQsMn+YIkMxnBJKvuiEFFmyhFdIA0jYf0pQNIHEDJKH8C7bU\nCukGabtNkDQL6U8pSP5xAMkJkCKRIRFsTR1dIW0ByUaAJJR3wfoc5UPiSzIDqRlHgCSVd3gv\npHlJYUhbQDISIAnlG97vaBaSx9EZ0nabJgmQilcjpDKS7ECakxSGlNzc/DcgcQMkoTzDhxxZ\nhJRPCJDIAVIkz4INQopL8jmqBtLM8IDUB0iRpgs28T2kaiFF3tQFJCdAilQMktdRI5DacQRI\nUk0WbJqjDSDVkQSkMo4qhzQePtERIFUSIAlVCpLfESBZC5CEGg2f6giQKgmQhCoEaRfYbaEC\nSIThAekYIEVyF2z6CgmQ6giQhCoHySupCUgNOQIkqZwFm/pebGDKDpDsBUhCARIgUQMk4vDl\nHQGStQBJKEACJGqARBs+2VEGpMgpZAFJIkASqgSksKNcSD5Jw/dPF4HUkqM6IRWRtNhkg8QK\nKQ3S4EIX0u4WIKUFSEIBEiBRk4N0PIJxI5BEHAGStUxCOh1TH5AAqZ5UIAVOY+44Wq3qhnQZ\nPt1R65CacqQCyXsSF0DiOEp7H2kG0viZDkiMFCD5TyvWKCShFRIgWcsipCa2kXQgkY+5OvOO\nLCBxMwmpnVk7KUcBSPSDF0chRT7bCkiBLG4jnQOkBEhUSfqQ2nJkctauHUgZjk6Qgi68kBjH\n0wekwi0FiWYHkEaOgjCCkIiSAKlwC0EivpprCFI/fO4KKeLCB4lzihdAKtwykAjzC4DkhxSA\nEYZEk6QOqTFHgCRVJqSrIwYkToBUOEAS6lf2Cil6Or7CkMZyxu8jFaYFSE7YRorUFZiziwRI\nxsKsnVBdliNAqi2TH6MApDlH1UNqzREgSdWJrpBOkNhnMwcksexAmrz4qxvSL3lI9D3rAEk8\nM5Cm0xF3DMnvaOjmBCldkvbe34A0qCQkzwR53ZDKr5AcN79Ze9YBkniAJJQIpBuc36wdgsxB\nas4RIAm16SQcAZLZ1CGd9Ph2fQCkKaQrnN/R/R5yHQESuyRIv+KtvtM7HZ3hcpQGz2/9t5q5\nB+ptfnY/09v9nnaB4/lVQts/uu2Sl2xpAKVSXiOtnNzffY/sWFRglST6kGz+ZqyRZue+/1f9\n2SiwRhrmfSrydqqLQors6mof0k9ACteeo+KQmHunXqz4blYxpM1fQAIkcp6nIvfzEtere250\nr5AojgDJWrqQoteudxupfw8JkACJWjak+LW/Rz58YR9S+mRD+5B2Zufe0htD+np96rru6fWL\ncNvsbaRZSKMrVwLpuFNDMiSSoxlIc+fr80By90oNQyqx9+odQHrpLr3M3zZ71o4Dyb0mINUG\nabj/ePOQPtbrl/fPwzef7y/d+mPutvnvI0XXX9+v15mauxdIvANEAtL+2+EF1fp59qlbviGk\n9/Xb4Ke39fvMbcu8IRv83fehtYognXZXTZ5sGEJiHiCyEkg7uWX/SH89VbghpOfR78Y/j1vi\nE7LOG033AMldIS0GadcEpMfu8bAy+vq27n4I/YVg6jutEiHVs410/vxECUjJx7W7U0jv3cPl\nm8NKoOt+rB8PmynPXffcb690x+d6/+/hf0/dY3/Z/nXdPbwFB2TkgXQYe00ae1FI1czalYYk\ncly7AKTrj+Wnv5eA9Nxdtkb6Seeuezx4+lr3L/XWXy6k5/Nlp8m1EpKmkF6PY78SbrvIwU/8\n8xF2IV0+0Jc42TB11AikwZ8SW/Zr59l82lJ66R77l3wvLqTHr8tln/uPbl3gb08hrb/t+3kH\nwm2XOYqQdz7iniB5JQGSpwuVvhOS/f6h//ezf803hPTjfNm6e56bUqP+7cH3D8cxj6vHb5Rt\nJ73Dcd0HpFiA5GkMaXiZ/7v3wwu/h88if9u5Hw/fLi8bzby0qwzS9VAN6pCin51tE9LTdRuJ\nCmm///FAeMeU0BDS18HQ+u2rn2ygOAIkT1dIaZMNVEfzkKKfQp/b1U4S0k5s2d9m7QZcRi/t\nPq8v+j77jae+ty5n6vqSO8bXywHRC2U/uz5AmnQ7dhAgLQ9p/9gdN0/en26QbpMN6+7b/uvx\nPNnQf/faX/ax/yEz2fB2eNVIpARIk8xAih8YpVVI1z0b+ldrJ0i36e/jNsvrGVJ/2Z6zHTOX\nA+n0DtK3h657orwzXArSdYLbnaC7Q0hkRzRIQUmKkHZiy/7Y+/MBzlM/8XyGdHtDdn94ufV6\nftH3+Xi7jLYdM9cQ0vUdpPcH0svGQpCGb7kOJVUIaXBUyKTJhmKQZo7V1S4kUkU2isZjDr4f\nvIP08US4bRlIq8DudICUDykkaW4PIUDijzn8nvwO0qlSB4h0A6QCkGba7gYBUpExB99zt7xK\nQJo4qhnS8HjfKZDojqqFtJNa9pykIe3J7yCdEoF0+1XdkFImG8Qg/TvaHfzOIUmk/TGKsKP6\nIDknoLgHSONbAVJaApCGv7o7SHOOxicaYyEaQtr+B0ilcyB9vhzf0Hp8oX2+UGCyAZAijkYn\nGisJaZwUpJ3UslfP2dduzTmGUOnp7wGk0ze1QXJPLZYw2TAPaXh+JEAylTtr93b45/Hr8+OJ\n9KHBom/Iuo76bwFp4giQzOa8IXvau/zpsG667kYbq+wuQq6jww+VQRqd65IPibBCGpxoDJBs\n5X4e6frv4rsIjX8GJK+jqyQuJHcsLUiXY34LQZq+KXlL5i/eGoJ57F/QvfUro/cF10iTaYY2\nIPEnGxiT35lvyJaCxE4akugTJt4Q0o/TRMN7v0JabhtpOl9X5TbS+CzmbEgsR4Dkywqk/Y/n\n7niEvT3tUF/BxZEy/z266B9AkoQ0FyBxk3hDNmWV5Lm8KkhjR4AUcQRIboUghQ4AXjck9mTD\ncpDmHQESuwCkrFm7Qp81vy9IPEeA5AuQqoc0cQRIEUcLQJpsdsv8xVsWXtoBEiAVaXCShclM\nsMxfvAVIRZpC4k42AFJ+t7P+TN+clPmLt0xAqn7WbuqIC4npCJB8XZ5Fnk8TRJ4wZT4u6576\n8uFyspgltpHGO6kCUtuQbo6EIU0/TrAwpI/jjg3HA7kuAOn63+l5R7YySB5HgGQX0ukY+5ev\nw3+nX8m5+9q99IfjetwvAen2H9omJOZkAyAViAapO/8z/Oq7jLeqmu79/dlLAiRO+ZC4jnIg\nERxJQBo4MgBpH4E0viKpKaSDpKdFIVW/jeRzBEh6kOYmG85P7ut5lPaDNdD1ic99bTe88vP5\nE+Y/usclt5Gqn7UDJGOQZqe/uyue/QjS9VN5+4w10ucF4TeaxlKzdoHGkIizmdSEIfEmGwCp\nRIw3ZLsgpALbSPvP5/OZYj4e7e0iRJ3NpFbswfQ64kFiO6oP0tCR7i5C1MmG9Jd23BaF5K6s\n7x2S5/PjgETdaXU0xR2a/s5YIzEDpGOAVBkkkZbes2Fuw6g+SH5HvMmG9iE5jlqHtMCeDYGZ\nbgIks9tIBSBxHG1zIVEcARK7ZfdsCL33SoFkddZOAFLsNMqnA3IBki8rkOT3bMiCVHi5FHow\nA45yIEXPRw5I4WxBktyz4Y4gcSYbJpBCkm5+0qe/VSC5jiLL/nyIsLTHwgqkyJ4N60OT2xbc\nRqp3X7vykCLnfj1B2gKSv1WktBHp0fZsWF//GVZu1s7rqwpIIUccSFNHIUmAZDbang3lIIV0\neSQBUgjStjZIYxitQwoGSKGCkBiTDQ6k0cHyfZD67wDJ92BEShuRnrONNPrd7ecbpOupyH4d\nin39fojz9QCJdf25v7/U15+HfF+7wOWer7v+6+9DlK+9JM71x1+3f/786b/+WfgrdXn2jGK/\njz2Zg/+v9vfvopDe18NDfr+t36/fM9dIaauk8YU1rJHCDx19jcTbreF8qqPkNRJphaS3RhJ6\nNJaFtP9Yr1/ePw/ffL6/dOuP2y+kIVU7awdIKY6ah9Sf/fKScxZZcUi+6oZEn2yQhDR94xaQ\nZBq/5vx6fTooenr9ci4FJH+RR64AJO+EAyARHw73sVkckj9A8lcC0i707qt/5q5CSNM5uAUg\nbTYuK5m/eIv4eSTeng2AxIIUkOSfA5+D5N4GkIxB8nb3kCKOyJMNmxCkyJ5C/wu/j7QFpIsj\nRxIg3QMkL5jIDg5RSM5tJpBoju4WkocDUQggZVQA0tGRz0tsT6EwpPFtLEDy7KYgDum8OwMb\nkidASr731GKOeJA8YGJ7CgUhzei7G0gbQGoGEnWygX8kLkCafUA2E0k3SN3l3/MOR92+G/4w\nPcjQfnRVfyIHPwEkHUjRHV61IPl2QBWGNNhXNQzpegi70XHtPIe4c64aSOTgJ/cBKepIZ400\nHyBdDgg5BrMf/rwP2wokcvATQCJDSnRUDyTvJyJkITkfnwhD6k4v2IZH/C4EqdjBTwCJPNmw\nMCSio6YhnV/WOWD2t5d4xSAVOPjJXUCKO0qFFJ0pACTKQzL6RB8VkqNmnwWp2GldACkVUnzK\nrUJI/s+6Ku/93Q3/Gf8Qm2yI/O3h74qd1qWMpPuERJQESLyHZAppOL1Nnv4O5/yu1GldAGkw\n2RC/HvlIXHVCChx84Q4+j8TpriHNOLpC2kSvOF0hOZL4+9olOjodcgSQ0gOkxOiQAtfcTCGN\n305N2GmVC+ksCJByA6TEiJA2IUiny32QtsOf64YUOqxW+5C+ni/HbCDcFpAidVdHfkk+SOOn\n/Twk6iQfIMn8xVsjME+dCiTvgYxNQ5pzNAfpfHkMUnS/ud+Xq1iGFDzOY/uQuu4b/bbFIPkP\nrd8ApOnu/A6kTfTAJzRILEmtQ4ok8xdvjSA9cLaZSkEKnOylAUjBqYbzoxt2dCsCafZDE7OO\nJoKqhqTZCM7nw8uX/4qe7hjSrKPTZENsqmEOUtzEDRJDEvVdpEKQwkfwbh/ScaeGpbeRmoUU\nnmo4F4I0D+T3//5H+fjRfUHaRZL5i7dMTDbUt420BKQokKTp7+YhpdyXQmeuqLMAABlaSURB\nVJmYbKhv1o4EKTzVMAOJsKapAFLkuds+pCeNyYZAhiHNOyLttBpfIUUlpUCiOwIkdmM4T8+f\n5NsCkhAkQvYhxZ667UPqdN6QvRdIo9sBUtkA6U4gjbeXCkCaHpMYkCxAYnW3kAiOvAc/Gc88\ntAwp+syVhTSc9a4dUglJzUGaTOEtC4nhCJDYjQ5+UuqlHSCFIA1um+FoBInEaVFI8SeuBUjj\nZ3gX/hUlQOJHceSBNHlbCZAKZ2WNxA2QIhGmvwGpcHcNybtTAyBVDmnmeasJ6XJI1ePhtc7H\nz78edit4LKHZ3Ct9vT72p2J+o93vHEgXP/7d7ACJCmnICZC4kG5q9sOfPUe3m825zvv6vIG0\n/qDc7wxIFz+BHb9NQyI5sgeJ4ygX0twLKdWXdlMtHj2OCy6kz657/nH4+vHUdZRPJaVDuvpp\nF9L8QfRzHAGSLxFIxNd2w+u8dM/n7y4HL44HSFYgUQKkS93A0iykuSOsDq93ad1ddlj97B4I\nty0AqcZtpBxIw9u2C2l2iqweSEnbSF3n+zZcMqTheqi6WTuaIz+kDSBlLfuZqNPfk5mEzg+p\nu/12Ng1IsddzNEgFHElD8u/9XWrHBj4klqM8SPPv2SjvtOpAcqa/9wM91+lvq2skoqMGIZXb\nQwiQfKXv/Z3zdup0BECilA7J3UNo6Gj0WVjC4UwAyVMSJPJWUDwXUrfEvnZ1QyI6YkEafaqc\ncmAgw5AIe+NYgkTedyGeAqTYTF07kGZ3WtWANNoCB6RyqexrR3JUM6QN4X2kG6TJyVwIkriQ\nTiukJSBRnrKA5KS397dxSJsESNvQj5mQzju0ApJwUpAKSDIJibZCOkiiQxode4t2+FRA8rSL\nJPMXbwESL9oK6QBpdqfVMaSt/8faIJGesXdw7G9OEpAGm09VQ9rQISXFhHSeswMkscxAmu56\nVyukDQlSnqNESOSSIdFeQgGSEx3S/DTdSZDzFpNFSOQVkiqk4acqAGmZFoE0/8bRyqluSKfm\nJhsAqa2WgETYlQGQpCCdWgoScXIMkJykIP0DSIUhcR0BEjsjkP6ZOrIIie6IC2kDSHVnZBtp\nIOl6Sd2QeJMNmw1Tkk1I1Lc9AenUL0Kr74MOPr7PdoY0f8XDVSn3QKDNT3LdzO93v4cdIP0u\n1wnS4ILtn2XayT8EpQGUysz7SNNXgE2vkSYrJOYqibfT6kJrJPJ+OGY5pGcI0vgVoD1IDEem\nILEdARI7S5BG79vWDWlmssGBdP6gEiBVnBik/L1W7wbSphFI9D2sAckJkMpC4kgCJGMBEj2O\nIwakzYYniXO2y0RHSZAYH/kBJCdAikSfbACkFgIkemUgbQaQtq4jmiRAspgupOgOD2FIJRxp\nQTodRejsqP8cbCuQOB/mBiSnbEjxXYesQWI5ikDaOJA2G64kQLKYJqSZnVnrhhSYbNgMIJGO\nu5UPKcFRAiTW0UUAyQmQ2JAuh4jMgXSMMf0NSEtkHJL/121Aoh13KwqJdGNAWiLb20iBK6hA\n4jkKnR/pLCnzY31nSDSGi0DiHTcOkJzEZ+1Cq6waIMXfRwKk1pKDlP1GEiDNQKK9MkxxxIbE\nPJApIDkBEiAB0jnTkGreRpKHRJysWAIS98jagOQkBOlix9asHdPRzE6rZbaRSAHSItmDdF0L\n2XofqVZISY6YkNinegAkJxFIt+2ihiFlOwIkawESMcOQovsMLQCJf+4hQHK6J0hcR/HJBuOQ\nqCd/AaRb5iAZ3UaqFVLSCokHKeFkeIDkJDdrhzUSIFWWCUjOJPf1tV0QUhFHgFQIUsrZWQHJ\naW5xUCW5b7s2AsnIZAMgLZQBSKMdgUxCYjsyMv2dNmfHgZR0unBAcpKBNL+NBEiAZC6DkC6b\nTICkBYnxPlKSI0Byk9lGulY3pMBkw/BgXIDUUBYgBXZNNQSJ7ygAaTM8Gpc8pFRHZEhpjgDJ\nrRgkf01B2gBS4rKvJECilA3pDGgzPBpXA5ASHQGSGyBFcicbzoCco3EBUksBEqVcSGc/GzlI\n//relU12RISU6giQ3O4GUoIjD6TN9Whc+Y4AyVqSkDIltQPpeji7c41ASnYESG6AFCm20yog\nJd/SbBVCKuPoDiClOyJBSncESG6ABEjiy76WAGm+FEcxSJtd6jkoLEHKcARIboAUKbzT6kYC\nki9AWjBAmk8AUrYkAqQMRwRIOY4AyQ2QUiBtAAmQ3O4EUpKjIKTNCVKuJG1IWY4AyQ2QIi08\na0eAxPncKyBxWxxS9NxigFQO0mSFVBJSniNAcptfHJ5TG0XPdglIgFRpC0OaOf8yBVIhR4CU\nASnTESC5AVKk2MFPxCANpjCmUw2AJBkgzZXmSAXScDJQElKuI0By09lGAqQYpKuknMlvQOK3\nNKT8WTtACkEanlI2z1EcUrYjQHIjQCKraQ6SwmTD8OTMgLRwsu8j0V/HAVI2pO3wNOeCkPId\nAZLb7DEbGDMLgJQNyVUFSMsGSDMlOpqFlLmz3QykTEcxSAUc3TWk9fQiQIo0M9mQu9sqIBmL\nCmmdAKmJbSQ5SFmS1CCVcHTHkNYpaySRWbtSjnQhbWUh5ToCJHaSL+32eR+kaB5SjiQtSEUc\nAdLh+n2/yK2+l25F/+NF2vxMq4vccHeZqf4t1PaPVLuFF/8kKQi5YY0UL3WF9LfbhG8qPf2d\nvUIKrpHKrJCwRnIDJECSXPZVNQdpvT7P1wESq023CUuqFlIhR/cI6RogMRTdzoSkASnfESCx\nE4aUI6laSJvN+PQTjUAq5QiQ3ABpDlLg1oBUaiA7yX6M4i4hnQSpfNS8lCM/pGKOAMlNA1Ix\nR7QHM32FpLf3NyBpBEjRUldILUIq5wiQ3AApUnOQCjoCJDdASoMkcTLmso4AiZ0epNk9w+uG\nFD2tS4WQSjoCJDfS4ghpmf+sEiABUj1pQSJ8etYApHRH8fMjCUIq4mgKqagjQHIDpARIp/Mj\n1QaprCNAcgOkSKHJBkAiLvvKwjZSrPKQTm8y5UsKQSrjaAypsCNAcsuClDZrV86R1hrpWP5G\nkh/Sv/9uycfJZ0Aq7QiQ3PIgzQZIgFRP4pDSJdUNKbbTqhykLf3MLXRIxR0BkhsgAZLcsq8s\nQIoFSCKOAMmteUgZjnQgbRkn5aNCEnAESG6AFEllsqHQ5Dcg8QOkSLVBKuZoAEnCESC5ARIg\niS372gKkSIAk4wiQ3IiLI1WSB1JBRw1ONpRzdIUk4wiQ3AAJkMSWfW0BUiQxSPmSloEk5AiQ\n3ADJFKSCjs6QpBwBkhsgRYpONgBScwFSuBxHLUAScwRIbtTFkSgJkFiQSjoCJHaAFK4oJHew\nGiDJOQIkt8UhlXQkDWk02TA6M0UFkAQdAZIbIJEhjU/xUh5SUUeAxA6QwhWGdB1uUwMkSUeA\n5EZeHGmSGoLknHXs+F2upDGkso7++yPqCJDcACnScLLBPX9fDZBkHQGSGyARITmdSAFSY9UE\nqagjXUjZh/8eQSrtSPiZDkhOjUPKchQ9ZLF1SDvpZzogOQFSpNhB9I1D2ok/0wHJib44kiS1\nCelSpqQe0u0A4qVXSIDEDpCCWYe03V4llV4hARI7QApWAaSLpJKQdqSFI7zs6wuQgslMNhSD\ntL1BKu4IkNhVBKmsozYgbQHJSMtASpIESDFI2+1VUnlHgMQOkILJQsqUNJz+LgjpskcDIHED\npGCykw0mIV33DAIkboAUrBZIAiskQGIHSMHuD9JtV1VA4rYQpBRJI0iFHTUDScIRILEDpFB5\njpabbCgGafjRCUDiBkihpCHlSbpCElkhARI7QAp1b5Ccz/IBEjdACnVnkNzPxAISt6UgJUiq\nG9LsZEMZSDKOAIldNZBKO9KHlCWpMKTxMRoAiRsghaoCktAKCZDYAVKoe4I0OWgQIHFLgvQr\npdX3rFZJfzSjzc+suvmr7H5nt/1Tot3Syzaj0gBKtdgaib9KsrhGoq+m5mftslZJvwuukDwH\nscMaiRsghfKZ2bQIyXcwSEDiVguk4o4SIZElOZACt8qFJOYIkNgBUijPk398cpZYw8kG363+\nry9dUjFI/oMTAxK35SCxJZmD5JxTYh7S7Zq+WxWAJLdCAiR2gBQqF5JzHhezkAJHywckboAU\nKOSIKunn9ar+W/1fnqRCkEJnnQAkboAUKPP92L+dOCRBR4DErhJI5R0tAmnzN7giswApfBYk\nQOK2ICSupKohbbqZ14HZkCQdARI7QAqUvUaiXS1Vkh8SDxcgFQyQAuVCIuy0WhzSlgUpdnpL\nQOIGSIGsQ/KukDiSoqeJBSRuS0JiSroLSKmSfJC2HEjx0y0DErc6IAk4qhxSwBFZ0sxpywGJ\nGyAFWmiyQQnSjCNAYgdIgZaClCZp+yfgiCZpzhEgsVsUEk8SILEgMZp1BEjsqoAk4cgMpBRJ\n2/9yIM07AiR2gBRoqcmG5SERHAESO0AKtBwkvqTDdlA6JIojQGK3LCSWJECSgERyBEjsaoAk\n4sgQJK6kfmLOD2l3LN8RILEDpEDLTTYsCYnoCJDYLQyJI+l+IPEkHd8pSoNEdQRI7AApUIuQ\nyI4AiV0FkGQcmYLEkpQMie4IkNgtDYkhqW5IxMmGDRvSaR+gBEgMR4DEDpACLQPp+mH0s6Rt\nHqRYHEeAxA6QAqlA2s5DOu+UyobEcgRI7OxDEnJkAtLg+Ci7E6RZSYmQeI4Aid3ikOiS6oZE\nmmwYHmhod3Q0B+nyKQkepB3TESCxA6RAS0Byj3i3O32eSAASlxEg8QOkQMtOf1MhXT+2x4HE\ndwRI7JaHRJb0XdSRPUgbgqQESOyXdYSFkxsgOQFSJM5Oq5dm3026fY6cDCmFESDxA6RAGpBm\nJfEhpTkCJHYKkKiSACnsiAgp6WUdYeHkBkhOS0ASc2QS0owkLqRURoDED5ACeU40xiLBn2yY\nlTQ80hYFUrojQGKnAYkoyRgk+nmYcyDFJPEgJb+sIyyc3ADJaQFIco4SILEklYfkHPpxFlIO\nI0DiB0iBxmoY52HOghSWxIKU5wiQ2KlAokmyB4kjKW2yISLJPRZxHFLWyzrCwskNkJzkIQk6\nYkLinNA8E9LfnZcSHVI2I0DiB0iBvJAYkjIgeVdKo4PjhyEVYARI/HQgkSSZgsQuC5JHEhFS\nEUaAxM80JElHs/c+U1LyZMNZ0i7qKASpDCNA4gdIoXQhjVdKJEiFVkeEhWN7eI2UIFEk3Tkk\nR9Lk9GEeSOUYARI/y5BEHdmHNHx5Nw+pJCNA4gdIoTIh5U02jChNz2f5R5IRIPHTgkSQJOuo\nCkg9pd0cpP6YkEsvHNvDawRIoWxA+nu0ND146h85RJSFY3t4jdQgzUpaKT+YZiD9/bvZTyz9\nkUNEWTi2h9cIkELpTzZc2hzvz26c5sKxPbxGepBmJK20H0xrkHj3PrO6h9cIkEKZgeRzpL1w\nbA+vERHS+tDkwtzFEZO0Un8wAana4TWiQVpf/xnWNqRMScUmG7yO1BeO6eE10oQUkbQqMXy8\nLEjzyABJb3iNGNtIgHR7ci8Hye9IfeGYHl4jLqSu71epVt/9rYr9hYw2P4NtNpFflm2jvRiM\nJQUhNzqk8pMN+9A6aVVq+FgZayTKZ2ULTTYEVkjqC8f08BopQ/JKWpUbPlI6JNKnzstACjlS\nXzimh9doDtJ13nvqqMzimEq67GOn/WACUq3Da0RdI3kc3SukDelAKEUgBR2pLxzTw2tEfUPW\nd2GZxTGWdN3pW/vBNPA+EiBVE/F9pLVv14ZCi2Pld6T+YOpDCjtSXzimh9dIcV+7a6sbpdXg\nQ0jqD2aWpAKQIo70F47l4TWyAKmntDoqcj7Lp/5gAlKlw2tkA9L+aEly+GnCkPInG2KO9BeO\n5eE1MgPJ4PCAVOnwGgFSOF1IUUf6C8fy8BoBUjhVSHFH+gvH8vAaAVI41ckGQKorQAqnCWnG\nkf7CsTy8RoAUKUcSIOkNrxEgRdKDNOfIwMIxPLxGgBQpB1LeZAMg1RYgRVKDNOvIwMIxPLxG\ngBRJC9K8IwMLx/DwGgFSJECqc3iNACmS0mQDwZGBhWN4eI0AKVaGJEDSG14jQIqlAoniyMLC\nsTu8RoAUSwMSyZGFhWN3eI0AKVYGpOTJBkCqMkCKpQCJ5sjCwrE7vEaAFC1dUiIkoiMTC8fs\n8BoBUrSlIVEdmVg4ZofXCJCipUNKm2wApFoDpGgLQyI7MrFwzA6vESDFS5aUAonuyMbCsTq8\nRoAUb0lIDEc2Fo7V4TUCpHjJkBImGwCp4gAp3oKQOI5sLByrw2sESDOlSmJDYjkysnCMDq8R\nIM20FCSeIyMLx+jwGgHSTKmQuJMNgFR3gDTTQpCYjowsHKPDawRIcyVK4kHiOrKycGwOr1Et\nkKZnfSk6fKQlILEdVf5MBySnBZf2alVckjAk1mQDIFVfHZBWq/KSDEHiO6r8mQ5ITvcBKVES\nA1KCo8qf6YDkBEhFIKU4qvyZDkhO97GNlAiJPNmQ5KjyZzogOd3HrF2iJCqkNEeVP9MByanu\npQ1IzQ6vESARSpFEhJToyNDCMTi8RoBEKAUSbbIh1ZGhhWNweI0AiVKCJBKkZEeWFo694TUC\nJEpCkNIdWVo49obXCJBI8SURIGU4MrVwzA2vESCR4kOan2zIcWRq4ZgbXiNAosWWNAspy5Gt\nhWNteI0AiRYgVTS8RoBEjCtpDlKeI2MLx9jwGgESMS6kmcmGTEfGFo6x4TUCJGpMSXFIuY6s\nLRxbw2sESNRKQsp2ZG3h2BpeI0Aix5MUg5TvyNzCMTW8RoBEjgcpMtlQwJG5hWNqeI0AiR5L\nUhhSCUf2Fo6l4TUCJEYcSUFIRRwZXDiGhtcoCdKvO23zM7vNRvs/ovJKAygV1kicGKsk/2RD\nmdVR4r2/m+E1AiRWdEleSMUcmVw4ZobXCJBY5UEq58jkwjEzvEaAxIssyQOpoCObC8fK8BoB\nEjOqpOmsXUlHRheOkeE1AiRmyZCKOjK6cIwMrxEgcSNKGkHalHVkdeHYGF4jQGJHk+RAKs3I\n7sIxMbxGgMSPJGk42VCckeGFY2F4jQApIYqkG6Tyq6O95YVjYHiNACmlzTylKyQJRqYXjv7w\nGgFSWrOSOsHV0d74wtEeXiNASmxO0k9JRtYXjvLwGgFSajOSfkoyMr9wdIfXCJCS820o3S7a\nSDKyv3BUh9cIkDLajC1dfj78wv69b3d4jQApr42D6fjDaV2Us2AJVbFwtIbXCJDy27idLgQk\nveE1AqRyDR0BkuLwGgFSuQDJyPAaAVKx3Jd2td37lobXCJAwfHPDawRIGL654TUCJAzf3PAa\nAZJQmGzQG14jQBIKkPSG1wiQhAIkveE1AiShAElveI0ACcM3N7xGgIThmxteI0DC8M0NrxEg\nYfjmhtcIkITCZIPe8BoBklCApDe8RoAkFCDpDa8RIAkFSHrDawRIGL654TUCJAzf3PAaARKG\nb254jQAJwzc3vEaAJBQmG/SG1wiQhAIkveE1AiShAElveI0ASShA0hteI0DC8M0NrxEgYfjm\nhtcIkDB8c8NrBEgYvrnhNQIkoTDZoDe8RoAkFCDpDa8RIAkFSHrDawRIQgGS3vAaCT/eORm+\na4Rw7+8rw0vM8F0jhHt/XxleYobvGiHc+/vK8BIzfNcI4d7fV1hiCBUIkBAqECAhVCBAQqhA\ngIRQgQAJoQIZhrQ+pH0fUqv5vtd+73WyC2l9/ae+ar7vtd97pQBJoprve+33Xim7kI5V+nA2\n8FSs+94vHyBJBEh3l21ItT6a9UOq+s5rBEgSAdLdZRHSdfa12kezekg133edLEK6VO+jWTuk\niu+6VoYhVfxoVg6p3nuul11I63XFb7BXfNcrX/Ja2YWEUEUBEkIFAiSECgRICBUIkBAqECAh\nVCBAQqhAgIRQgQAJoQIBUoG6Y+uXz9MPvqu8OJe+P59vc7zyt4fu4e18rfXr4ctX99D/9Pwu\neq9RyQCpQBcU3fs+AOnFufRjfb3N4btvx296Sa/d+7futb/2idD6Y4E7j4oESAU6Kfl87tZf\n/is8dw6k9dv5m/fu237/0H0cvun3bVsfrnT45rxC2u/fsMNbNQFSgS5KnvvVSf/D4X9P3dP+\n86F76mmt1x9DSC9XH+sHZ4jzTV96Xqffvyxw71GJAKlAFyU/useLhqfDOuiw7dN1z4fLX5wX\nfF/dhcflJVy/auovO6+Rbntev3SBVRyyFiAV6KrkvErZ934Omz4v/T+jqxy3hE7fXF/CHVdf\nx998e+/e3rrLK7+Dr1fxO4+KBEgF8kD67P/5uv1qAOmp+zx983p9CffycJG0fj2skL7Os3f7\nz9PFyH6AVCAPpP3wn70D6frterDwv11f7x2+u8zehebSkb3wQBXo8nT/6FcgZEjHLarJpfuH\n7uu8rbQHpHrCA1Wgy9P9qd+6IUO6bAutjzMKl0v7aYfrEIBUTXigCnR7H2lPgHTZRnrqfhy/\nvvSv4t6O03v74wppf10jYRupmgCpQNc9Gz72BEiXWbuH89z24ZVcv4vDSdfx1eFp9m6PWbuK\nAqQCnRQ9vHydfpiBdHkf6XrRYVXWPZ9n8k6rqdfzrB3eR6omQFq+F/JC77BnQy0BkkLXfe1m\nwr529QRICn0QgWDv73oCJI3enynXwueRKgqQECoQICFUIEBCqECAhFCBAAmhAgESQgUCJIQK\nBEgIFej/AXrN4iAs8qxQAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_ind(iris.pca,\n",
    "             # show points only (nbut not \"text\") 只显示点而不显示文本，默认都显示\n",
    "             geom.ind = \"point\", \n",
    "             # 设定分类种类\n",
    "             col.ind = iris$Species,\n",
    "             # 设定颜色\n",
    "             palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n",
    "             # 添加椭圆 Concentration ellipses\n",
    "             addEllipses = TRUE,\n",
    "             legend.title = \"Groups\",\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，调色板的允许值包括：\n",
    "+ 灰色调色板的“grey”;\n",
    "+ brewer调色板例如“RdBu”，“Blues”，......; 要查看全部，请在R：中键入RColorBrewer::display.brewer.all()。\n",
    "+ 自定义调色板，例如c（“蓝色”，“红色”）;\n",
    "+ 科学期刊调色板（来自ggsci R package），例如：“npg”，“aaas”，“lancet”，“jco”，“ucscgb”，“uchicago”，“simpsons”和“rickandmorty”。这些\"npg\"，\"jco\"都是杂志或者出版集团的缩写，具体搜索就行。\n",
    "\n",
    "例如，要使用jco（临床肿瘤学杂志）调色板，请输入以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iS5Z8MdQeJMNkwghSTd/KRPf6tAch1Flv35EGFpj4UVSJE9G9aHJrctuI1U7752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\n10iLaemQnl0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_ind(iris.pca,\n",
    "             label = \"none\", # hide individual labels\n",
    "             habillage = iris$Species, # color by groups\n",
    "             addEllipses = TRUE, # Concentration ellipses\n",
    "             palette = \"jco\"\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 图表定制  \n",
    "注意，fviz_pca_ind()和fviz_pca_var()相关的函数是核心函数的包装fviz()[在factoextra ]。fviz（）是函数ggscatter()[在ggpubr]中的包装器。因此，可以在fviz_pca_ind（）和fviz_pca_var（）中指定要传递给函数fviz（）和ggscatter（）的其他参数。在这里，我们提供了一些额外的参数来定制变量和观测量的PCA图。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.1 基础要素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**维度**  \n",
    "默认情况下，变量/观测量在主成分1和2上表示。例如，如果要在主成分2和3上可视化它们，则应指定参数axes = c(2, 3)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nPRj9PeAqSSm6jJG2zfvrofycFIkks6RgfJArVuiaxB4vNNt3p6Z2FqWR3NXKi3\nb9V49G+yffvK9r8YWcGmnozS8nH7FmlduwZptTDpRsK2swpnnyggRUkKRvVkEEBiVUHLjRIl\nSNXfq8qGv3iwevM6R5yKNJVpn5AAiVDephb3Of0N+p2aHruz+h/n967tVXjzKtI0pgCJWQXZ\nDGHzNej3aUB6dT90eJyUOldrv7dMnV79Tt3Wp9UHKOtAHcXcVChIdByxq6D50zt6kLoHf93r\na6byWc/1bPeDp0ZoW204YlekKUwBEr8KmitTS1OLKpmAVD2Q5bd8WMS9fLbrrVzqQeq3WnHE\nsEgTmHYpCZAIFWY60yhFBClTl8e3euepesKrtlO31cJdOQXqJu6mAInOis7UWKy2put10oL0\nVf+oXzzLvvD+mkjfqd1qcWLXbmRZpLFNRYJEyBHTCjKVKz1IT/V/2xfPSwHL0wxSv3XRu9vG\ns0gjmwIkQi9CU8PpnbXpaqX03d//23/Su5qsZHRq9+m2PqrTQKtPZVqkkU3bpARIzEwnRWtv\nunrLtL0he2pGNpzUb543D1BuOxuyYt333G19r0wxrm1jW6RRTQFSBJGYjhslSpDaIUJNZ8Nv\n/bp8cGXX/X0r/70Mti746pv4FmlMU4AUQUSmw9J1MF0dfdAOWm1vyD6Lpqd+pPL3qtS5vCGb\n37KSnXbr+W7LEesijWcqECRKjnhXkG9+2g0UGu64fmVlu4l1kcYz/QnxBEhxTZXnpYfV+Iac\nCiTvc1AH8TcFSPQiNPXsDLO5LTv0XL2wst7CvUgjmQIkelGado0SNUjlPvYg2XPEv0jjmAIk\netGaeo28sWuSCEAKuOHlIgGmPwGeW4BEypGECvIaC0oMkgNHIoo0hilAIhe1qRpdz1i9x2YP\npb/wMQsZy+QkAaYAiVz0psrd1KYbLhSkoNG1ThJgCpDIFcFUOU+FQAiSE0diihQg0XsSK4qp\nc1lbdGiHgTSzWk6REpv++HtuABItRyIqqDF1LWwLkP70F7PvceNIUpHS2gEkakUydX0g63qP\n9hgk4yc4ciSqSEkFkKgVzdStvB1A6jgyvMdsM28uq0gJBZCoFc/UrVFa7YobgFRhNH2LK0fS\nipROAIlaMU3pSNI7G1Q7iHXyDmeOmBZp8xOs60db9+f6zZQ3b3gbN8UB6Xu/lL8Mu3/nd3FV\nGSMxR0xrfcnUpeodQFLjVUsWi748i7T/NePA1Auki/lNUUC6tYGrm5ezSQCpFhFJA5Bajsbv\ncOeIaZHWR/g96/noC9Lcm35yapBeWXZ7luh/njeVvby8pwJIjewr3xqk7q8a7+NmmnMt0ib5\nvyobmPIG6dlMg1brkT3Nu7kKILWyr/6FHbXOBv0ukhrt42JZiWeRtteEvIwAACAASURBVEVW\n/3vLVHbrQXqcVJey33pO2fykvvU8ZLfuXQ1E1YmW6TPIQbquvPbUHz1HTGt93ZSApAlIdj12\nq5/Ms0gHLdK5YuHcgtS8bPa8VFOSfdSlnfX8thlIkQSQNNk2SiEgeXHEtEjr8vpU10gPdX7n\n77P673r1o5yDrLh6ak6cntVl1E29io3NRGRDkBJeI9W6Z2pwjhcqgDRQKEk+INngy7NIu167\nb3nWVvZfv9X/qQ/orMqu5W/RBNXKykYrq07wPs/7eXOQ6obx7mVsFEAayo6kpSZJDfZZ7/m2\n+kSeRarfR2pA6Llo1Oz6KBqjV/UYm3O7flOQsqJdzJ/Z0i5uAkgj2Z3eLTRJA5DWGyQ7cnkW\nqV5WKyB91bU4sytaqas6PZ4fe5DK9CQF6VSdbVbnnL+El1F/9BwxrXVr0yCSOpAsGyTLquRZ\npHry16d2r/bU7qRGowau6lNNO1u959uh80kOklKn3/aeLOmpHUAay6ZR8gPJlyOmRaoXVNvZ\n8F9dZ8On/NteI5XPBK1QU8U5Xj3/eT8Tev3/zHgdapC+t/KJOt+ys4GQI4BklD9J3XOTjWd2\n61dMM+JZpINvHGP3d9ZfPp3qe0nt8JyXNhN6jVVzr2ksapDqZ/pmN8JxdqUAklHrGe4DkjdH\nTIt02HSPbsjeTkrVw1mbG7Rlv3denuOp8+tZnuZ1M6FXj147KfOlPz1IZTBZNU87oQCSWeun\nd+YdlkDy52gXReopcpDqO0i/BecX84hzLwGkOa2leTBILl1G+yhSL1GD1N1BKtpA9NqlMF0r\nZuPmtrOBlqO9FKmPqEHS7iC9LuZdfASQFuRB0jxIIRztp0g99EPc/U19B6kWQFrScnGHgORY\nkfspUncRg0R+B6kWQFrU8umdYaMOEh1HeypSZxGDlFPfQaoFkFa0lPJGkP66TWp2V+cTC4lF\nSnUpTw1SHAGkNS3lw3STFUjuVSyzSGd+YuQogLQXUxeSGpDGl0iBHMktUgKWIoFE2fdd6I+e\nI7m1Pqf5Il8Caa5B8qlAsUU6+8tXewGk/ZjOF/p0tELXGM00SF71J65I+19REDRJxKO/NQVE\nNRJAstNckVuBFMzR1kdvrWmGEuQqLUhngLSp6VyhT+6ztiCZGyTPqtv66NfUJ+b41I7AnBak\n/KGqwQ04tdvK1JKkKUjhHDE4eqMM3+x/w+3BH5GTg5S/T+ryBUjbmc4NUx2+/MtHZ3YEHHE4\nek1LZ0aD7m/fDxiKGqRy4Gr2BEjbmZrLfnSvtQHJ1CD5Vx2Ho9dP4KxMqTKVHqT8nZFeIOUA\nyVHm4XWDV2OQSK67N+6zdLg0jxBpBJCq3xd6uc4JILnJVPzDu60jkGj6r7YZsuvTtRUjUr8c\nTTyyASA5yjQsSH/RXB9NQAqq15RHH9Y3LAkkdH9varpMUgMSLUfpBiEEXzoAJEJPektWptPy\nV8ONfYNExFHMoye+OSkJJEoBJB9NxzP0i0aQQiuV/ugj3NyvBJAIPektuZmO80/rUvjTzuyo\nOCI8eo0fXkW6IIC0Y1M188oEUniVBh+9qQHiVqSzogXpdVKn+oEuuEbiYDpDkg4SGUfhgxBI\nTZfEHqRXVR7VE9AAEgvTYTV0Z3E9SMqwm6dcA7W6AuJXpDMiBal6LNozK0kCSExMlWG5fIyj\n3iDR1JXrIARSUyexB6l5zGBJEkDiYjp4QFC3Sm+Q0nQq+3XBsSxSkyKAVJB0AUiMTCcjgDqQ\nKDlaG4RAahom9iBdq8fZlk/pPAMkRqZjkqondLcvySpqGCjRPSCuRToRKUifttx+8QtZVqZ9\nbdT8/HXD7QjvZPzVn0R7D5VtkY5F2/39uTbPj3mdCUGKwJGcCiIyHdx2bUCi5CjSGATORTqU\nhBuyAIlAbY6r5sVglFCAq8BBCACJUGIqiM5U7+9WgRyZGiDeRx/bFCAdx7QnSXVNkqPDLgYh\nACRCiakgStO+h0G7JWv3RosrIO5HH9eU+D6SpoCgRgJIZGpIsuTIrS75H31MU1KQbrMgZYVM\ny1YCSHSqL466G0kz+3h9Fwo4+oimtKd2N/Uyrs+6P8NlOwEkSlWNkZGSwHMJEUcfzZT4Gulq\nJgQgMTIdc0R1Mi7j6GOZUnc2ZDfj2u4PQNretIGG+mJWyNFHMk3TazcDUvdd+FcI/6b7F+VO\n/u/P2n4xQbIUWiRq1RgRm4o5ev4t0nXuNUDiZdqMa6BlSczR8wfpmT20V4/s2S4CJF6m9SVS\nns903/lJzNHzByl/Zdnt+SkWPs+byvqucIDEy3Q47wkRS2KOXgBI+j1ZvfsOIPEybTw7gkga\nJjFHLwKk/Hu/FLVyuX8Ha9vRDJm2bC2AFM1zdDuJxpRSYkwxaPWIpr3naL6uEJbEHD1AIpSY\nCooM0qQu/RsmMUcPkAglpoIidja0Lya16ceSmKMHSIQSU0HxQTJWp0fDJOboARKhxFRQCpBm\n6tORJTFHf1iQMB0XtQwnczM7OsAk5ugBEqUnvaUgU4Pn4u/8fE3DJcY0Bkg3pa4fL1+zAFIK\nzyVcrBomMUcvAKRHpm55eUNWZYQkAaQ0nsusrLIk5uj5g/QoEbqo33JpPBI8QAApkecaKssN\nk5ij5w/SST3zpzpVe2Dub8ams5VjN7OQeYuYo+cPUlXEl1u3SCSARK2lngWbtxv3EnP0/EE6\ntZMIfe84teNsukSL3TegoWESc/T8QfrtZmpHZwNr00VYrE8mRiyJOXr+IOW/p/rf7Pad2cNH\nACmxp9tcxpamfhJjihuyRzRd83S6wm1ZEnP0AInSk95SkOm6p1sNVw2TmKMHSJSe9JaCTC08\nnbtd6ef3KiWmSKlB+l7bORt8I5oKIFHLqnLcf0Zx5MnyqEG6KIDE39S2i9vJ9K950yEny/vx\n81y6offrG8usABK1rDu4XUy7QI84WR45SKcIJ8oAiVrWleRCxCBQKpikFCk5SJ8T6S2kSgBp\nQ097HCamR5osjxykcnQDrpG4m7p4Wtek+UdOB5ksjxwkdDZIMHXztKzKOdNDTJaHzoZDmjp6\nBv/UfP+T5UVokXxDmRdAopb7zVaLndYG8O15srwfT8+FIrmQTtdQ6S/ChFxCKiiSqXtKW7zD\nYtzRbifLowdJxbhGAkjE8qic9Qq1C3Sfk+UBpGOaelXO2pusA93hZHkRTu0iCCDx8FxJfyfT\nnU2WB5COaerruVjvrqZ7miyPGKSiYHBqJ8HU25N6Oq7VRBFSpADpmKYBnvP16t3MyZ8s78f7\nbJk4kGUBJGr5dTYo/Z+pRoG6fJVKnywPIB3UNASk2XcHgFTvL3eyPHqQvvdzOWvxwzskkwAS\ntcJAmmEkPFCxk+WRg/TMmguk7OUf1UQAiVqBIJkNaAIVOVkeNUgfpa7v4t/XRSnCXyUBJBae\neobfiu/KZmrqz6VZ/Ltl6vzqTwBV+5Zm6a6ye/XOW72uXDJmibzJ8qhBunUTFV+r0iISQGLh\nqYF0rk47ztXK6iSkrO1/DrtrxyDdy23Pc7N38zqb/zBJk+VRg5SpdsDqR538QjLpL8JjZGVU\nUCzTQJAe6vzO30o9ypXnb/E6a1eeZkGq9mv+VgAWe5/VwtW0oMnyqEHSvrNo7yMBJAaefZWe\nVHkC/66B+DSbztXK+VO7er9v9/pVWax830aY4CunL9Ifb0+AJNuUpteuP5ProclnQeo3mvae\n0x/N1A8TU1IBpKOaUoG0hAYZSDl5wwSQSD3pJcY0EKRTcxZ3apuL/tTuPQXpYwaptjivfGh/\n9O4sXRv3c9sHdledVYvm95qp5tkp2qKjIoCkycvYqDJGapLE5DxHkOp+hbO618Mr603tSh2k\nTP3m37MZpPNaZ0OlwdG7ptWpjLDAp7kQezbvfmsZelJd56G26CiAdFRTz86GrmL17u8uv7Xu\n7xadW/n6MgNSa+EWqUtmvcsujVfd+NUTxTWrL+0ez6ar8TVYdBU5SJEEkFh46t+Qt6y7Idv9\n1W7IdugUq65z10gXla0PJTNFav81/VDZN2tavYu6NW979O3gteLmWd7Z0hZdBZCOakruqTrT\nrkN7OdltUZiL1JKl4vKobfWK07wpSOeqP/5T7qMtjodhrOhnKdBlpQeJmiQxOS8DpKqV+C0u\n2C9t5n3igpTbNUxFe5T13QfN/hf1vCp1/fZrRv2I42EYKwJI1BJjGqP+2uHKtXfxXb74XHsC\nkJoPXd7h0l8PaSDVkX7nQRoOw1gRQKKWGNMo9Vcm5+n2rb2v6rQ4vyERSPlKw/QsG5bxh9az\nAd/qIX/dhsHicBjGigAStcSYRqm/v8aW1Nzu6OdgKs7sXtq5XbtTczWXzYPU7w2QLDypJcY0\nEkh11m0AUikTS+fixO7S97GPQFJtD8O372z4Np0N+W5BIiZJTM4L6WxoTalJcop03DBVo9Lz\nrOuka7ZmDTKn8vyu7vO+DhYBkqsnscSYRgy0HupAbOogjaV3PcS8uyPbQlFdHRV/H+1d2HN5\nHaUtuoH04xdoHZHXu3wFkCR4dqakJPlE2g//uVevuzFCDRTff3QDM/J6boTTcBEguXoSS4xp\nnED1AUJkpp7vKyLpbsV2o1bbwP5zy9SpPt/7lCNVq1tK2iJAcvUklhjTWJ0NeXs7idjUS/Nx\n0BapNJBoSRKT89JA4mVqZgkgkXvSSozpYUDKjQ0TaaQ/IZ4ASbbpkUAqNWKp66inEECilxhT\nMYHSmeoNU3M1R2MsDyRSknjXemxTMYHSmrYwNaY0WQyQ6CXGVEygEX45pfrJ8ijSuE1KgHRE\nUzGBxrlCbM/yABKdJ6nEmB6ts2FqqqjG1wKkCBJjCpDy5tZxsJdEkChJklbrtAJItcJJ6lIS\nIBFKjClAspvyYV0Aic5KoKmYQGOZkg0HBEh0VgJNxQTK3hQg0VkJNBUTKHfTPiNFgURIEvMK\nimwqJlDupgCJeQVFNkVnA5EAEvMKimwKkIgEkJhXUGRTgEQjLR9lgURHEu8Kim0KkGjECqQ/\nF/047Q1BUeWSjtFBskCtX0SLxNbzgKZ6Nm7fIq0LIEnwPKCpYJDISGJdQdFNxQTK2xQgMa+g\n6KbobCARQGJeQdFNARKFBrkoDSQqkjhXUHxTgEQhgDT2pJIYU4BEIYA09qSSGFMxgbI2BUhj\nTyqJMRUTKGfTYSaKA4mIJMYVlMBUTKCcTQHS1JNIYkzFBMrZFCBNPYkkxhSdDQSSDhINSYwr\nKIEpQArXKA0BEqHEmAKkcAEkkyeNxJgCpHDJB4mEJL4VlMJUTKCMTQGS0ZNEYkzFBMrXdJyE\nAIlQYkzFBMrXdA8gUZDEtoKSmIoJlK8pQJrxpJAYU3Q2BAsgzXhSSIwpQArVJAUBEqHEmAKk\nUO0DJAKSuFZQGlOAFCqANOtJIDGmYgLlajpNQIBEKDGmYgLlaroXkMJJYlpBiUzFBMrU1JB+\nAIlQYkzFBMrUFCAteYZLjCk6G8K0H5CCSeJZQalMAVKQTMkHkAglxhQgBQkgLXsGS4wpQAqR\nMfekghRKEscKSmcqJlCWpgBpzTNUYkzFBMrSFCCteYZKjKmYQDmamjNPLEiBJDGsoISmYgLl\naAqQ1j0DJcYUnQ3+msk7gEQoMaYAyV8AycIzUGJMAZK/dgdSGEn8KiilKUDy1lzWASRCiTEV\nEyg/U4Bk5RkmMaZiAmVnOpt0gkEKIolbBaU1FRMoO1OAZOkZJDGmYgJlZwqQLD2DJMYUnQ2e\nmk85ySCFkMSsghKbAiRPASRrzxCJMQVIflpIONEgBZDEq4JSmwIkPwEkB88AiTEVEygz092C\n5E8SrwpKbSomUF6mS9kGkAglxlRMoLxMdwySN0msKii5qZhAWZku5hpAIpQYU3Q2+GjXIPmS\nxKmC0psCJB8BJFdPX4kxBUgeWs408SB5ksSogjYwBUgeAkjunp4SYyomUEamK3kmHyQ/kvhU\n0BamYgJlZAqQfDz9JMZUTKCMTPcPkhdJfCpoC1MxgfIxXUsygEQoMabobHDWEUDyIYlNBW1i\nCpBctZpiAIlQYkwBkquOAZIHSVwqaBtTgOSo9QQDSIQSYyomUC6mRwHJnSQmFbSRqZhAmZha\npBdAIpQYUzGBMjE9DkjOJPGooK1MxQTKw9QmuQASocSYorPBSUcCyZUkFhW0mSlAcpFVagEk\nQokxBUgOssus3YDkSBKDCtrQFCA5CCCFezpKjKmYQBmYWubVfkByI2n7CtrSVEygDEwBEoWn\nm8SYigl0e1PbrNoRSE4kbV5Bm5qKCXRzU+ucAkiEEmOKzgZbHRIkF5K2rqBtTQGSpewzCiAR\nSowpQLLUQUGKftx7MQVIdor+zQyQZJuKCRQgjZQV0pezpZ3H4nNtuBdTMYFuaxq/98oVpKz7\no/1rLYAkwXN/pgluTLIFKfL9s72YigkUIA2lg+TMEUAiFzobLJRi8GYQSP0lkir1R6sfYr9d\nirrQdyniTKIHSVu2E59R73sxRYu0riQ/cAu6RhovrwogUQsgrYsZSPV5XEqQYv4yeC+mYgLd\nzjTNJCCcT+1izlWxF1MxgW5mmmh+t1CQ3HruAJIEz32ZMgWpG9mQacvWco4x2nx+ezEVE+hW\npqnmwOY61q5VrKma92KKzoYVAaRaAGlZAGlZyZ4TxB2kWI+z2YspQFpUukdAsgcp0pMK92IK\nkBYFkHoBpNSe+zH14Wi3IMV5mvuKxJiKCRQgUcovxuXi2E2ts/HcjakXRwCJUmJMxQQKkCjl\nWZiLBbKXWvcTOhvm5cfRnkFaLJKd1LqnANKsPDkCSJQSYwqQZgWQDFoolH3Uuq8A0px8Odo3\nSAvFsotaZ+W5C1NvjgASpcSYigk0sak/RzsHab5kdlDrzDz3YAqQZjVXNDuodWaeOzAN4Agg\nUUqMKTobTArhaPcgzZWO+FoPEkAyKIij/YM0Uz7Saz1MAMkggLQsgDQVQJoqjKMDgGQuIeG1\nztBTuGkgR0cAyVhGsmudo6ds01COABKlxJiKCRQgUSq4MA2lJLrWWXqKNg3m6BggGcpJcq2H\nC50NQ4VzBJAoJcYUIA1EwNFBQJoWldxapxBAGggg2WtcVnJrnUIASRcFR4cBaVxaYmudradY\nUxKOjgPSqLyk1jpfT6mmNBwBJEqJMRUTKECiFFFhDkpMaK0z9hRqSsTRkUAalJnMWqcSOhta\nUXF0KJD0UhNZ62QCSI3IODoWSFq5Sax1OgGkRgDJU13BSax1OgGkWnQcASRKiTEVE2hcU0KO\njgZSV3byap27pzxTSo4OB1JbeuJqnb2nPFOAFKSfCJ6NxJiKCTSmKSlHBwSpLkBptU4rdDZQ\nc3REkKoiFFbrxAJI1BwdEqSyEGXVOrUAEkCiEECK4Cnn6EtTao6OCVJRjKJqXYSnKFNyjg4K\nUv4jqdZleEoypefoqCBF+ErKBaWSmEDjmMao/KOCFONLSU4qiQk0zpconyLdAUgxvpbEpNKh\nOxviXCAfFyQ2F5wbmB4ZpEhdtgCJ1pReAIlUse7GHxgkLvcSNjA9LkjRxocdGSQmd7c3MBUT\naJRxloyKdCcg8Ri4uIGpmECjjPxnVKR7AYnFUPoNTMUESmsa81edBweJw4+7NjAVEyipadR5\nBgBSBFNSobOBSHGnkDo6SAwmwNjA9IggRZ7U8PAgbT8l0wamBwQp9jS7AGnzSQI3MD0gSLFN\nAdLm8z9vYComUDLT6E9QAEj51k8k2MBUTKBUpvGf6QOQSm36jJwNTMUESmSa4HGNAKnSlo8/\n3MBUTKA0pikeIAyQav1s92TrDUyP1dmQ5JH2AKkVAUlsU2msQ4E0rViAFNUznCSuqTTRkUAy\nVCtAiusZTBLTVJrqQCCZKhUgRfYMJYlnKiXy5GlqrFI+RbpTkEJJYplKqTxZmporlE+R7hWk\nQJI4plIyT46mM9XJp0h3C1IYSQxTKZ0nQ9O5yuRTpPsFKYgkfqk0o2N0NsxWJUBK4RlAErtU\nmtMhQJqvSICUxNN/kAO3VJrVEUBaqEWAlMjTlyRmqTSvA4C0VIcAKZWnJ0m8UimxJyvT5bMK\nPkVKCdIfR/1sHQAUIo7VFx0kC9Q28PRqkzh9Jyf3ZGS6Vnl8inT/IHmRxCeVNvDkY7padXyK\n9AAg+ZDEJpXWtOfOBotOV4CU1NOdJCaptK4dg2RTawApraczSTxSyUL7BcmqzgBSYk/XW7Ms\nUslGuwXJrsIAUnJPN5I4pNJmngxMbb/4+BTpYUByI2n7VNrQc3tT67riU6THAcmJpM1TaUvP\nzU3ta4pPkR4IJBeStk6lTT03NnW5nuVTpEcCaeNvOnQ2WGnzU3CAtC7rLzuAtJXp9p1CAMlG\nG/YGASQLMbhNAZCsZNc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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dcbL8jGdXoJIqk0lszmrYw2hwlOTbJIN+sLk0VX9bfkl84TRj8Iclb2wmuY6bSZ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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variables on dimensions 2 and 3\n",
    "fviz_pca_var(res.pca, axes = c(2, 3))\n",
    "# Individuals on dimensions 2 and 3\n",
    "fviz_pca_ind(res.pca, axes = c(2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘图元素：点，文本，箭头**  \n",
    "参数geom（对于几何）和导数用于指定要用于绘图的几何元素或图形元素。\n",
    "1. geom.var：一个文本，指定用于绘制变量的几何参数。允许值是c（“点”，“箭头”，“文本”）的组合。\n",
    "    + 使用geom.var = \"point\"，仅显示点；\n",
    "    + 用于geom.var = \"text\"仅显示文本标签；\n",
    "    + 使用geom.var = c(\"point\", \"text\")同时显示点和文字标签；\n",
    "    + 使用geom.var = c(\"arrow\", \"text\")显示箭头和标签（默认）。\n",
    "    \n",
    "例如，键入以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BICUwO0PWW6nu82u2OzbWqnv0tn2u1S86K8/am9fbB+enmkReqOIF13rhvJsVAk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\n8v/FHu/18OsFzgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show variable points and text labels\n",
    "fviz_pca_var(res.pca, geom.var = c(\"point\", \"text\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. geom.ind：一个文本，指定用于绘制观测值的几何体。允许值是c(\"point\", \"text\")的组合。\n",
    "    + 使用geom.ind = \"point\"，仅显示点;\n",
    "    + 用于geom.ind = \"text\"仅显示文本标签;\n",
    "    + 使用geom.ind = c(\"point\", \"text\")同时显示点和文本标签（默认)。\n",
    "    \n",
    "例如，键入以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iWmXD4fCZH2SEGkxJTFDqjt8w7TZ5dFCeWTKcu/ZJWM++7PZpdFCeWTKasmgXv+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\nIhESIYhESIQgEiER8v8BHf8pvzTk1C8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show individuals text labels only\n",
    "fviz_pca_ind(res.pca, geom.ind =  \"text\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘图元素的大小和形状**  \n",
    "+ labelsize：文本标签的字体大小，例如：labelsize = 4。\n",
    "+ pointsize：点的大小，例如：pointsize = 1.5。\n",
    "+ arrowsize：箭头的大小。控制箭头的粗细，例如：arrowsize = 0.5。\n",
    "+ pointshape：点的形状，pointshape = 21。键入ggpubr::show_point_shapes()以查看可用的点形。\n",
    "\n",
    "具体例子如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4DkANI36e6a+Bze02oKpCwbPfS0MPAAkKg58t762Zqiq6cuIXcL0lmdm85Zmgwv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\nCYIIBJAgiEAACYIIBJAgiEAACYII9P8Bypsiyn8e/wwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l0AkmPVJKlYJ/D2JptwA6SvcAFIjpWOPSBK0akIIH2FC0ByrOlh78XXv28JSSu1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\n/w/cmEfIyzktZAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the size of arrows an labels\n",
    "fviz_pca_var(res.pca, arrowsize = 1, labelsize = 5, \n",
    "             repel = TRUE)\n",
    "# Change points size, shape and fill color\n",
    "# Change labelsize\n",
    "fviz_pca_ind(res.pca, \n",
    "             pointsize = 3, pointshape = 21, fill = \"lightblue\",\n",
    "             labelsize = 5, repel = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**椭圆的添加**  \n",
    "正如我们在上一节中所描述的，当按组着色观测值时，可以使用参数添加点集中椭圆addEllipses = TRUE。请注意，该参数ellipse.type可用于更改省略号的类型。可能的值是：\n",
    "+ \"convex\"：绘制一组凸包。\n",
    "+ \"confidence\"：将组平均点周围的置信椭圆作为函数coord.ellipse()[在FactoMineR中]。\n",
    "+ \"t\"：假设多变量t分布。\n",
    "+ \"norm\"：假设多元正态分布。\n",
    "+ \"euclid\"：绘制一个半径等于水平的圆圈，表示距离中心的欧氏距离。除非coord_fixed()应用，否则此椭圆可能不会显示为圆形。\n",
    "\n",
    "该参数ellipse.level也可用于以正常概率改变浓集中椭圆的大小。例如，指定ellipse.level = 0.95或ellipse.level = 0.66。具体例子如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fGIFAUTkQKP7ZrLvBWv50f15MXjIFKk+Nu5BiaKFB2f8YHFhqLh0l2R7s2w2lyu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\nEAkgAogEEIH/B46ceJ3zok2bAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MlBSnWkHFJZkw1FQLpJAiQjQPJUFCSq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\n1yI36wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add confidence ellipses\n",
    "fviz_pca_ind(iris.pca, geom.ind = \"point\", \n",
    "             # 使用iris数据集\n",
    "             col.ind = iris$Species, # color by groups\n",
    "             palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n",
    "             addEllipses = TRUE, ellipse.type = \"confidence\",\n",
    "             legend.title = \"Groups\"\n",
    "             )\n",
    "# Convex hull\n",
    "fviz_pca_ind(iris.pca, geom.ind = \"point\",\n",
    "             col.ind = iris$Species, # color by groups\n",
    "             palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\n",
    "             # 用凸包多边形代替椭圆\n",
    "             addEllipses = TRUE, ellipse.type = \"convex\",\n",
    "             legend.title = \"Groups\"\n",
    "             )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**轴线**  \n",
    "该参数axes.linetype可用于指定轴的线型。默认为“虚线”。允许的值包括“blank”,“solid”, “dotted”等。要查看所有可能的值，请ggpubr::show_line_types()在R中键入。要删除轴线，请使用axes.linetype =“blank”.具体使用如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d3lWwESvTRTumaJYlXg5hMUxhFAIipV2kh/PDxTgETKUZysBzB1UiiQ4q40ZPXb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\nBPo/dlNRsOLd7RIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_var(res.pca, axes.linetype = \"dotted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**图形参数**  \n",
    "要轻松更改任何ggplots的图形，可以使用函数ggpar[ggpubr package]。可以使用ggpar（）更改的图形参数包括：\n",
    "+ 主要标题，轴标签和图例标题\n",
    "+ 标题位置。可能的值：“top”，“bottom”，“left”，“right”，“none”。\n",
    "+ 调色板。\n",
    "+ 主题。允许的值包括：theme_gray（），theme_bw（），theme_minimal（），theme_classic（），theme_void（）。\n",
    "\n",
    "具体使用如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yn4/MZ3cVYVqT6s6whzvh3iIVJa8UtFCjTJd3FMRTqdhkzqbPNDInWsOSOS6/jw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\nRAIQgEgAAhAJQAAiAQhAJAAB/wf+fQpjKoyugQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind.p <- fviz_pca_ind(iris.pca, geom = \"point\", col.ind = iris$Species)\n",
    "ggpubr::ggpar(ind.p,\n",
    "              title = \"Principal Component Analysis\",\n",
    "              # 下标题\n",
    "              subtitle = \"Iris data set\",\n",
    "              # 说明\n",
    "              caption = \"Source: factoextra\",\n",
    "              # x,y轴标题\n",
    "              xlab = \"PC1\", ylab = \"PC2\",\n",
    "              # 标题名字位置\n",
    "              legend.title = \"Species\", legend.position = \"top\",\n",
    "              # 主题和配设\n",
    "              ggtheme = theme_gray(), palette = \"jco\"\n",
    "              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Biplot**  \n",
    "Biplot为主成分分析双标图。注意biplot图可能仅在数据集中存在少量变量和观测量时才有用; 否则最终的情节将无法辨认。另请注意，观察值和变量的坐标不是在同一空间上构建的。因此，在biplot图中，您应该主要关注变量的方向，而不是它们在图上的绝对位置。粗略地说：biplot展示了两方面内容：\n",
    "1. 根据前两个主成分，每个观测的得分；\n",
    "2. 根据前两个主成分，每个变量的载荷。  \n",
    "\n",
    "其具体说明意义如下：\n",
    "+ 向量在横坐标的投影就是对主成分2的载荷（系数），在纵坐标的投影就是对主成分1的载荷。（具体轴对应哪个变量看轴名）。但是这些都是近似，因为坐标轴有所不同；\n",
    "+ 点代表的是观测值，点之间的距离，反映它们对应的样本之间的差异大小，两点相距较远，对应样本差异大；两点相距较近，对应样本差异小，存在相似性；\n",
    "+ 两向量余弦值的绝对值大小反映两向量间的相关性大小，值越大表明两个向量对应的属性之间相关性越高。当两个向量近似垂直时，两个属性之间相关性很弱，几乎互不影响。  \n",
    "\n",
    "详细解释见：  \n",
    "+ https://stats.stackexchange.com/questions/2038/interpretation-of-biplots-in-principal-components-analysis\n",
    "+ https://blog.bioturing.com/2018/06/18/how-to-read-pca-biplots-and-scree-plots/  \n",
    "\n",
    "要制作观测量和变量的biplot.请键入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5ezSqJ2Jz3Z7IHX0NaRnFeWZLZGoshZi6rbSXdZN9JjXqzJQcXaKNcgC/IWH8YF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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_biplot(res.pca, repel = TRUE,\n",
    "                col.var = \"#2E9FDF\", # Variables color\n",
    "                col.ind = \"#696969\"  # Individuals color\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，使用iris.pca输出，让我们：\n",
    "+ 制作观测量和变量的双重图\n",
    "+ 按组更改观测量颜色：col.ind = iris $ Species\n",
    "+ 仅显示变量的标签：label = \"var\"或使用geom.ind = \"point\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RzUw+W16a46dyv1XtxtukqEQ4Zbx/r5UtYdKC+OA6ma5k4gpElJoZDsjsIgdY+N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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_biplot(iris.pca, \n",
    "                # 观测量颜色\n",
    "                col.ind = iris$Species, palette = \"jco\", \n",
    "                # 添加椭圆\n",
    "                addEllipses = TRUE, label = \"var\",\n",
    "                # 线条颜色\n",
    "                col.var = \"black\", repel = TRUE,\n",
    "                legend.title = \"Species\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在下面的示例中，我们希望按组为观测量和变量着色。诀窍是对各个观测点使用pointshape = 21。可以使用参数fill.ind通过颜色填充此特定点形状。使用 col.inds设置各个点的边框线颜色设置为“black”。使用参数col.var按组对变量着色。要自定义个体和变量颜色，我们使用辅助函数fill_palette()和color_palette()[在ggpubr包中]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8vwr6oxUUQP7USJZC8n6pqQzWTpaka5RPs3kOdy/Vn0HCn98W3brdFQsoFnaJdz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\nnA7ImwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_biplot(iris.pca, \n",
    "                # Fill individuals by groups\n",
    "                geom.ind = \"point\",\n",
    "                # 点的形状\n",
    "                pointshape = 21,\n",
    "                # 点的大小\n",
    "                pointsize = 2.5,\n",
    "                # 按照组类特定形状\n",
    "                fill.ind = iris$Species,\n",
    "                col.ind = \"black\",\n",
    "                # Color variable by groups\n",
    "                # 颜色\n",
    "                col.var = factor(c(\"sepal\", \"sepal\", \"petal\", \"petal\")),\n",
    "                # 标题\n",
    "                legend.title = list(fill = \"Species\", color = \"Clusters\"),\n",
    "                repel = TRUE        # Avoid label overplotting\n",
    "             )+\n",
    "  ggpubr::fill_palette(\"jco\")+      # Indiviual fill color\n",
    "  ggpubr::color_palette(\"npg\")      # Variable colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个复杂的例子是按组（离散颜色）和变量对主要成分（渐变颜色）的贡献对个体进行着色。另外，我们将使用参数alpha.var通过贡献率来改变变量的透明度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yBwq8rl5oonTKFgHTsvoFyH3OS15U7MRDTfxbhYVOyMQqGQbDgw6tCavZWVlRTt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\nV7zjd1EgGFIHOzUZllX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Z1twXCL+V0o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6ALAMwQ7QRm+oItIBgH0IdoBOaaeruuvYUagDAM783gcAAAd7SURBVFsR7ADNMs5Y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\nAAAASxDsAAAALPH/AZqyDR+RPaxBAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fviz_pca_biplot(iris.pca, \n",
    "                # Individuals\n",
    "                geom.ind = \"point\",\n",
    "                fill.ind = iris$Species, col.ind = \"black\",\n",
    "                pointshape = 21, pointsize = 2,\n",
    "                palette = \"jco\",\n",
    "                addEllipses = TRUE,\n",
    "                # Variables\n",
    "                alpha.var =\"contrib\", col.var = \"contrib\",\n",
    "                gradient.cols = \"RdYlBu\",\n",
    "                \n",
    "                legend.title = list(fill = \"Species\", color = \"Contrib\",\n",
    "                                    alpha = \"Contrib\")\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**过滤结果**  \n",
    "如果您有许多个体/变量，则可以使用参数select.ind和select.var仅显示其中一些观测量/变量。select.ind, select.var:选择要绘制的个体/变量。允许的值为NULL或包含参数name，cos2或contrib 的列表：\n",
    "+ name：是一个包含要绘制的个体/变量名称的字符向量；\n",
    "+ cos2：如果cos2在[0,1]中，例如：0.6，则绘制cos2> 0.6的观测量/变量；\n",
    "+ if cos2 > 1，例如：5，然后绘制具有最高cos2的前5个活动观测量/变量；\n",
    "+ contrib：如果contrib> 1，例如：5，则绘制具有最高贡献的前5个观测量/变量。\n",
    "\n",
    "具体使用如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YBxEym5G0fEgkJFajq97XlOf0uFOoiU4o0jYkEpUnZQYydSojeOiAWNtGn3y007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\nAKFIDAMIRWIYQCgSwwBCkRgGkP8AsWtlMe2g468AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize variable with cos2 >= 0.6\n",
    "# 可视化cos2>0.6\n",
    "fviz_pca_var(res.pca, select.var = list(cos2 = 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kh/h/Nj8UXLJCKS5QZvhMmGPjuLRRnzGCK1BzVwH+ktLg0ST6SdJqGMeQyRONnw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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select by names\n",
    "# 根据名字显示\n",
    "name <- list(name = c(\"Long.jump\", \"High.jump\", \"X100m\"))\n",
    "fviz_pca_var(res.pca, select.var = name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S/NZd6iqsMQSTZljEjF9NrM7Y8Xk77m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\nP8wckdRQR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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据前五贡献\n",
    "# top 5 contributing individuals and variable\n",
    "fviz_pca_biplot(res.pca, select.ind = list(contrib = 5), \n",
    "               select.var = list(contrib = 5),\n",
    "               ggtheme = theme_minimal())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.2 导出结果  \n",
    "**将图表导出为PDF / PNG文件**  \n",
    "该factoextra包生成基于ggplot2的图形。要保存任何ggplots，标准R代码如下：  \n",
    "``` R\n",
    "pdf(\"myplot.pdf\")\n",
    "print(myplot)\n",
    "dev.off()\n",
    "```\n",
    "\n",
    "在以下示例中，我们将向您展示如何将不同的图表保存为pdf或png文件。\n",
    "1. 创建您想要的图作为R对象：\n",
    "```\n",
    "# Scree plot\n",
    "scree.plot <- fviz_eig(res.pca)\n",
    "# Plot of individuals\n",
    "ind.plot <- fviz_pca_ind(res.pca)\n",
    "# Plot of variables\n",
    "var.plot <- fviz_pca_var(res.pca)\n",
    "```\n",
    "\n",
    "2. 将这些图导出为单个pdf文件，放入同一个pdf文件：\n",
    "```\n",
    "# Create a new pdf device 创建新的pdf文件\n",
    "pdf(\"PCA.pdf\") \n",
    "print(scree.plot)\n",
    "print(ind.plot)\n",
    "print(var.plot)\n",
    "# Close the pdf device 关闭文件\n",
    "dev.off()\n",
    "```\n",
    "\n",
    "3. 要将每个绘图打印到特定的png文件，R代码如下所示：\n",
    "```\n",
    "# Scree plot\n",
    "scree.plot <- fviz_eig(res.pca)\n",
    "# Plot of individuals\n",
    "ind.plot <- fviz_pca_ind(res.pca)\n",
    "# Plot of variables\n",
    "var.plot <- fviz_pca_var(res.pca)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**将结果导出到txt / csv文件**  \n",
    "PCA的所有输出（个体/变量坐标，贡献等）可以使用函数write.infile()[在FactoMineR ]包中一次导出到TXT / CSV文件中：\n",
    "```\n",
    "# Export into a TXT file\n",
    "write.infile(res.pca, \"pca.txt\", sep = \"\\t\")\n",
    "# Export into a CSV file\n",
    "write.infile(res.pca, \"pca.csv\", sep = \";\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 总结  \n",
    "本文我们描述了如何执行和解释主成分分析（PCA）。我们使用PCA（）函数[FactoMineR] 计算PCA 。接下来，我们使用factoextra R包来生成基于ggplot2的PCA结果可视化。无论你决定使用什么函数，在上面的列表中，factoextra包可以处理输出以创建漂亮的图，类似于我们在FactoMineR的前面部分中描述的三个函数：\n",
    "```\n",
    "fviz_eig(res.pca)     # Scree plot\n",
    "fviz_pca_ind(res.pca) # Graph of individuals\n",
    "fviz_pca_var(res.pca) # Graph of variables\n",
    "```\n",
    "\n",
    "当然R语言还有其他PCA函数具体见：\n",
    "1. [PCA使用prcomp和princomp](http://www.sthda.com/english/wiki/pca-using-prcomp-and-princomp)\n",
    "2. [PCA使用ade4和factoextr](http://www.sthda.com/english/wiki/pca-using-ade4-and-factoextra)\n",
    "\n",
    "本文主要参考文档有：  \n",
    "+ http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials  \n",
    "+ https://blog.csdn.net/u012845311/article/details/69943793#t3  \n",
    "+ https://blog.csdn.net/LuohenYJ/article/details/78355954"
   ]
  }
 ],
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